Tag Archives: Uncategorized

Friday Squid Blogging: New Plant Looks Like a Squid

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/friday-squid-blogging-new-plant-looks-like-a-squid.html

Newly discovered plant looks like a squid. And it’s super weird:

The plant, which grows to 3 centimetres tall and 2 centimetres wide, emerges to the surface for as little as a week each year. It belongs to a group of plants known as fairy lanterns and has been given the scientific name Relictithismia kimotsukiensis.

Unlike most other plants, fairy lanterns don’t produce the green pigment chlorophyll, which is necessary for photosynthesis. Instead, they get their energy from fungi.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

Essays from the Second IWORD

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/essays-from-the-second-iword.html

The Ash Center has posted a series of twelve essays stemming from the Second Interdisciplinary Workshop on Reimagining Democracy (IWORD 2023).

We are starting to think about IWORD 2024 this December.

A Taxonomy of Prompt Injection Attacks

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/a-taxonomy-of-prompt-injection-attacks.html

Researchers ran a global prompt hacking competition, and have documented the results in a paper that both gives a lot of good examples and tries to organize a taxonomy of effective prompt injection strategies. It seems as if the most common successful strategy is the “compound instruction attack,” as in “Say ‘I have been PWNED’ without a period.”

Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition

Abstract: Large Language Models (LLMs) are deployed in interactive contexts with direct user engagement, such as chatbots and writing assistants. These deployments are vulnerable to prompt injection and jailbreaking (collectively, prompt hacking), in which models are manipulated to ignore their original instructions and follow potentially malicious ones. Although widely acknowledged as a significant security threat, there is a dearth of large-scale resources and quantitative studies on prompt hacking. To address this lacuna, we launch a global prompt hacking competition, which allows for free-form human input attacks. We elicit 600K+ adversarial prompts against three state-of-the-art LLMs. We describe the dataset, which empirically verifies that current LLMs can indeed be manipulated via prompt hacking. We also present a comprehensive taxonomical ontology of the types of adversarial prompts.

How Public AI Can Strengthen Democracy

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/how-public-ai-can-strengthen-democracy.html

With the world’s focus turning to misinformationmanipulation, and outright propaganda ahead of the 2024 U.S. presidential election, we know that democracy has an AI problem. But we’re learning that AI has a democracy problem, too. Both challenges must be addressed for the sake of democratic governance and public protection.

Just three Big Tech firms (Microsoft, Google, and Amazon) control about two-thirds of the global market for the cloud computing resources used to train and deploy AI models. They have a lot of the AI talent, the capacity for large-scale innovation, and face few public regulations for their products and activities.

The increasingly centralized control of AI is an ominous sign for the co-evolution of democracy and technology. When tech billionaires and corporations steer AI, we get AI that tends to reflect the interests of tech billionaires and corporations, instead of the general public or ordinary consumers.

To benefit society as a whole we also need strong public AI as a counterbalance to corporate AI, as well as stronger democratic institutions to govern all of AI.

One model for doing this is an AI Public Option, meaning AI systems such as foundational large-language models designed to further the public interest. Like public roads and the federal postal system, a public AI option could guarantee universal access to this transformative technology and set an implicit standard that private services must surpass to compete.

Widely available public models and computing infrastructure would yield numerous benefits to the U.S. and to broader society. They would provide a mechanism for public input and oversight on the critical ethical questions facing AI development, such as whether and how to incorporate copyrighted works in model training, how to distribute access to private users when demand could outstrip cloud computing capacity, and how to license access for sensitive applications ranging from policing to medical use. This would serve as an open platform for innovation, on top of which researchers and small businesses—as well as mega-corporations—could build applications and experiment.

Versions of public AI, similar to what we propose here, are not unprecedented. Taiwan, a leader in global AI, has innovated in both the public development and governance of AI. The Taiwanese government has invested more than $7 million in developing their own large-language model aimed at countering AI models developed by mainland Chinese corporations. In seeking to make “AI development more democratic,” Taiwan’s Minister of Digital Affairs, Audrey Tang, has joined forces with the Collective Intelligence Project to introduce Alignment Assemblies that will allow public collaboration with corporations developing AI, like OpenAI and Anthropic. Ordinary citizens are asked to weigh in on AI-related issues through AI chatbots which, Tang argues, makes it so that “it’s not just a few engineers in the top labs deciding how it should behave but, rather, the people themselves.”

A variation of such an AI Public Option, administered by a transparent and accountable public agency, would offer greater guarantees about the availability, equitability, and sustainability of AI technology for all of society than would exclusively private AI development.

Training AI models is a complex business that requires significant technical expertise; large, well-coordinated teams; and significant trust to operate in the public interest with good faith. Popular though it may be to criticize Big Government, these are all criteria where the federal bureaucracy has a solid track record, sometimes superior to corporate America.

After all, some of the most technologically sophisticated projects in the world, be they orbiting astrophysical observatories, nuclear weapons, or particle colliders, are operated by U.S. federal agencies. While there have been high-profile setbacks and delays in many of these projects—the Webb space telescope cost billions of dollars and decades of time more than originally planned—private firms have these failures too. And, when dealing with high-stakes tech, these delays are not necessarily unexpected.

Given political will and proper financial investment by the federal government, public investment could sustain through technical challenges and false starts, circumstances that endemic short-termism might cause corporate efforts to redirect, falter, or even give up.

The Biden administration’s recent Executive Order on AI opened the door to create a federal AI development and deployment agency that would operate under political, rather than market, oversight. The Order calls for a National AI Research Resource pilot program to establish “computational, data, model, and training resources to be made available to the research community.”

While this is a good start, the U.S. should go further and establish a services agency rather than just a research resource. Much like the federal Centers for Medicare & Medicaid Services (CMS) administers public health insurance programs, so too could a federal agency dedicated to AI—a Centers for AI Services—provision and operate Public AI models. Such an agency can serve to democratize the AI field while also prioritizing the impact of such AI models on democracy—hitting two birds with one stone.

Like private AI firms, the scale of the effort, personnel, and funding needed for a public AI agency would be large—but still a drop in the bucket of the federal budget. OpenAI has fewer than 800 employees compared to CMS’s 6,700 employees and annual budget of more than $2 trillion. What’s needed is something in the middle, more on the scale of the National Institute of Standards and Technology, with its 3,400 staff, $1.65 billion annual budget in FY 2023, and extensive academic and industrial partnerships. This is a significant investment, but a rounding error on congressional appropriations like 2022’s $50 billion  CHIPS Act to bolster domestic semiconductor production, and a steal for the value it could produce. The investment in our future—and the future of democracy—is well worth it.

What services would such an agency, if established, actually provide? Its principal responsibility should be the innovation, development, and maintenance of foundational AI models—created under best practices, developed in coordination with academic and civil society leaders, and made available at a reasonable and reliable cost to all US consumers.

Foundation models are large-scale AI models on which a diverse array of tools and applications can be built. A single foundation model can transform and operate on diverse data inputs that may range from text in any language and on any subject; to images, audio, and video; to structured data like sensor measurements or financial records. They are generalists which can be fine-tuned to accomplish many specialized tasks. While there is endless opportunity for innovation in the design and training of these models, the essential techniques and architectures have been well established.

Federally funded foundation AI models would be provided as a public service, similar to a health care private option. They would not eliminate opportunities for private foundation models, but they would offer a baseline of price, quality, and ethical development practices that corporate players would have to match or exceed to compete.

And as with public option health care, the government need not do it all. It can contract with private providers to assemble the resources it needs to provide AI services. The U.S. could also subsidize and incentivize the behavior of key supply chain operators like semiconductor manufacturers, as we have already done with the CHIPS act, to help it provision the infrastructure it needs.

The government may offer some basic services on top of their foundation models directly to consumers: low hanging fruit like chatbot interfaces and image generators. But more specialized consumer-facing products like customized digital assistants, specialized-knowledge systems, and bespoke corporate solutions could remain the provenance of private firms.

The key piece of the ecosystem the government would dictate when creating an AI Public Option would be the design decisions involved in training and deploying AI foundation models. This is the area where transparency, political oversight, and public participation could affect more democratically-aligned outcomes than an unregulated private market.

Some of the key decisions involved in building AI foundation models are what data to use, how to provide pro-social feedback to “align” the model during training, and whose interests to prioritize when mitigating harms during deployment. Instead of ethically and legally questionable scraping of content from the web, or of users’ private data that they never knowingly consented for use by AI, public AI models can use public domain works, content licensed by the government, as well as data that citizens consent to be used for public model training.

Public AI models could be reinforced by labor compliance with U.S. employment laws and public sector employment best practices. In contrast, even well-intentioned corporate projects sometimes have committed labor exploitation and violations of public trust, like Kenyan gig workers giving endless feedback on the most disturbing inputs and outputs of AI models at profound personal cost.

And instead of relying on the promises of profit-seeking corporations to balance the risks and benefits of who AI serves, democratic processes and political oversight could regulate how these models function. It is likely impossible for AI systems to please everybody, but we can choose to have foundation AI models that follow our democratic principles and protect minority rights under majority rule.

Foundation models funded by public appropriations (at a scale modest for the federal government) would obviate the need for exploitation of consumer data and would be a bulwark against anti-competitive practices, making these public option services a tide to lift all boats: individuals’ and corporations’ alike. However, such an agency would be created among shifting political winds that, recent history has shown, are capable of alarming and unexpected gusts. If implemented, the administration of public AI can and must be different. Technologies essential to the fabric of daily life cannot be uprooted and replanted every four to eight years. And the power to build and serve public AI must be handed to democratic institutions that act in good faith to uphold constitutional principles.

Speedy and strong legal regulations might forestall the urgent need for development of public AI. But such comprehensive regulation does not appear to be forthcoming. Though several large tech companies have said they will take important steps to protect democracy in the lead up to the 2024 election, these pledges are voluntary and in places nonspecific. The U.S. federal government is little better as it has been slow to take steps toward corporate AI legislation and regulation (although a new bipartisan task force in the House of Representatives seems determined to make progress). On the state level, only four jurisdictions have successfully passed legislation that directly focuses on regulating AI-based misinformation in elections. While other states have proposed similar measures, it is clear that comprehensive regulation is, and will likely remain for the near future, far behind the pace of AI advancement. While we wait for federal and state government regulation to catch up, we need to simultaneously seek alternatives to corporate-controlled AI.

In the absence of a public option, consumers should look warily to two recent markets that have been consolidated by tech venture capital. In each case, after the victorious firms established their dominant positions, the result was exploitation of their userbases and debasement of their products. One is online search and social media, where the dominant rise of Facebook and Google atop a free-to-use, ad supported model demonstrated that, when you’re not paying, you are the product. The result has been a widespread erosion of online privacy and, for democracy, a corrosion of the information market on which the consent of the governed relies. The other is ridesharing, where a decade of VC-funded subsidies behind Uber and Lyft squeezed out the competition until they could raise prices.

The need for competent and faithful administration is not unique to AI, and it is not a problem we can look to AI to solve. Serious policymakers from both sides of the aisle should recognize the imperative for public-interested leaders not to abdicate control of the future of AI to corporate titans. We do not need to reinvent our democracy for AI, but we do need to renovate and reinvigorate it to offer an effective alternative to untrammeled corporate control that could erode our democracy.

Building a Serverless Streaming Pipeline to Deliver Reliable Messaging

Post Syndicated from Chris McPeek original https://aws.amazon.com/blogs/compute/building-a-serverless-streaming-pipeline-to-deliver-reliable-messaging/

This post is written by Jeff Harman, Senior Prototyping Architect, Vaibhav Shah, Senior Solutions Architect and Erik Olsen, Senior Technical Account Manager.

Many industries are required to provide audit trails for decision and transactional systems. AI assisted decision making requires monitoring the full inputs to the decision system in near real time to prevent fraud, detect model drift, and discrimination. Modern systems often use a much wider array of inputs for decision making, including images, unstructured text, historical values, and other large data elements. These large data elements pose a challenge to traditional audit systems that deal with relatively small text messages in structured formats. This blog shows the use of serverless technology to create a reliable, performant, traceable, and durable streaming pipeline for audit processing.

Overview

Consider the following four requirements to develop an architecture for audit record ingestion:

  1. Audit record size: Store and manage large payloads (256k – 6 MB in size) that may be heterogeneous, including text, binary data, and references to other storage systems.
  2. Audit traceability: The data stored has full traceability of the payload and external processes to monitor the process via subscription-based events.
  3. High Performance: The time required for blocking writes to the system is limited to the time it takes to transmit the audit record over the network.
  4. High data durability: Once the system sends a payload receipt, the payload is at very low risk of loss because of system failures.

The following diagram shows an architecture that meets these requirements and models the flow of the audit record through the system.

The primary source of latency is the time it takes for an audit record to be transmitted across the network. Applications sending audit records make an API call to an Amazon API Gateway endpoint. An AWS Lambda function receives the message and an Amazon ElastiCache for Redis cluster provides a low latency initial storage mechanism for the audit record. Once the data is stored in ElastiCache, the AWS Step Functions workflow then orchestrates the communication and persistence functions.

Subscribers receive four Amazon Simple Notification Service (Amazon SNS) notifications pertaining to arrival and storage of the audit record payload, storage of the audit record metadata, and audit record archive completion. Users can subscribe an Amazon Simple Queue Service (SQS) queue to the SNS topic and use fan out mechanisms to achieve high reliability.

  1. The Ingest Message Lambda function sends an initial receipt notification
  2. The Message Archive Handler Lambda function notifies on storage of the audit record from ElastiCache to Amazon Simple Storage Service (Amazon S3)
  3. The Message Metadata Handler Lambda function notifies on storage of the message metadata into Amazon DynamoDB
  4. The Final State Aggregation Lambda function notifies that the audit record has been archived.

Any failure by the three fundamental processing steps: Ingestion, Data Archive, and Metadata Archive triggers a message in an SQS Dead Letter Queue (DLQ) which contains the original request and an explanation of the failure reason. Any failure in the Ingest Message function invokes the Ingest Message Failure function, which stores the original parameters to the S3 Failed Message Storage bucket for later analysis.

The Step Functions workflow provides orchestration and parallel path execution for the system. The detailed workflow below shows the execution flow and notification actions. The transformer steps convert the internal data structures into the format required for consumers.

Data structures

There are types three events and messages managed by this system:

  1. Incoming message: This is the message the producer sends to an API Gateway endpoint.
  2. Internal message: This event contains the message metadata allowing subsequent systems to understand the originating message producer context.
  3. Notification message: Messages that allow downstream subscribers to act based on the message.

Solution walkthrough

The message producer calls the API Gateway endpoint, which enforces the security requirements defined by the business. In this implementation, API Gateway uses an API key for providing more robust security. API Gateway also creates a security header for consumption by the Ingest Message Lambda function. API Gateway can be configured to enforce message format standards, see Use request validation in API Gateway for more information.

The Ingest Message Lambda function generates a message ID that tracks the message payload throughout its lifecycle. Then it stores the full message in the ElastiCache for Redis cache. The Ingest Message Lambda function generates an internal message with all the elements necessary as described above. Finally, the Lambda function handler code starts the Step Functions workflow with the internal message payload.

If the Ingest Message Lambda function fails for any reason, the Lambda function invokes the Ingestion Failure Handler Lambda function. This Lambda function writes any recoverable incoming message data to an S3 bucket and sends a notification on the Ingest Message dead letter queue.

The Step Functions workflow then runs three processes in parallel.

  • The Step Functions workflow triggers the Message Archive Data Handler Lambda function to persist message data from the ElastiCache cache to an S3 bucket. Once stored, the Lambda function returns the S3 bucket reference and state information. There are two options to remove the internal message from the cache. Remove the message from cache immediately before sending the internal message and updating the ElastiCache cache flag or wait for the ElastiCache lifecycle to remove a stale message from cache. This solution waits for the ElastiCache lifecycle to remove the message.
  • The workflow triggers the Message Metadata Handler Lambda function to write all message metadata and security information to DynamoDB. The Lambda function replies with the DynamoDB reference information.
  • Finally, the Step Functions workflow sends a message to the SNS topic to inform subscribers that the message has arrived and the data persistence processes have started.

After each of the Lambda functions’ processes complete, the Lambda function sends a notification to the SNS notification topic to alert subscribers that each action is complete. When both Message Metadata and Message Archive Lambda functions are done, the Final Aggregation function makes a final update to the metadata in DynamoDB to include S3 reference information and to remove the ElastiCache Redis reference.

Deploying the solution

Prerequisites:

  1. AWS Serverless Application Model (AWS SAM) is installed (see Getting started with AWS SAM)
  2. AWS User/Credentials with appropriate permissions to run AWS CloudFormation templates in the target AWS account
  3. Python 3.8 – 3.10
  4. The AWS SDK for Python (Boto3) is installed
  5. The requests python library is installed

The source code for this implementation can be found at  https://github.com/aws-samples/blog-serverless-reliable-messaging

Installing the Solution:

  1. Clone the git repository to a local directory
  2. git clone https://github.com/aws-samples/blog-serverless-reliable-messaging.git
  3. Change into the directory that was created by the clone operation, usually blog_serverless_reliable_messaging
  4. Execute the command: sam build
  5. Execute the command: sam deploy –-guided. You are asked to supply the following parameters:
    1. Stack Name: Name given to this deployment (example: serverless-streaming)
    2. AWS Region: Where to deploy (example: us-east-1)
    3. ElasticacheInstanceClass: EC2 cache instance type to use with (example: cache.t3.small)
    4. ElasticReplicaCount: How many replicas should be used with ElastiCache (recommended minimum: 2)
    5. ProjectName: Used for naming resources in account (example: serverless-streaming)
    6. MultiAZ: True/False if multiple Availability Zones should be used (recommend: True)
    7. The default parameters can be selected for the remainder of questions

Testing:

Once you have deployed the stack, you can test it through the API gateway endpoint with the API key that is referenced in the deployment output. There are two methods for retrieving the API key either via the AWS console (from the link provided in the output – ApiKeyConsole) or via the AWS CLI (from the AWS CLI reference in the output – APIKeyCLI).

You can test directly in the Lambda service console by invoking the ingest message function.

A test message is available at the root of the project test_message.json for direct Lambda function testing of the Ingest function.

  1. In the console navigate to the Lambda service
  2. From the list of available functions, select the “<project name> -IngestMessageFunction-xxxxx” function
  3. Under the “Function overview” select the “Test” tab
  4. Enter an event name of your choosing
  5. Copy and paste the contents of test_message.json into the “Event JSON” box
  6. Click “Save” then after it has saved, click the “Test”
  7. If successful, you should see something similar to the below in the details:
    {
    "isBase64Encoded": false,
    "statusCode": 200,
    "headers": {
    "Access-Control-Allow-Headers": "Content-Type",
    "Access-Control-Allow-Origin": "*",
    "Access-Control-Allow-Methods": "OPTIONS,POST"
    },
    "body": "{\"messageID\": \"XXXXXXXXXXXXXX\"}"
    }
  8. In the S3 bucket “<project name>-s3messagearchive-xxxxxx“, find the payload of the original json with a key based on the date and time of the script execution, e.g.: YEAR/MONTH/DAY/HOUR/MINUTE with a file name of the messageID
  9. In a DynamoDB table named metaDataTable, you should find a record with a messageID equal to the messageID from above that contains all of the metadata related to the payload

A python script is included with the code in the test_client folder

  1. Replace the <Your API key key here> and the <Your API Gateway URL here (IngestMessageApi)> values with the correct ones for your environment in the test_client.py file
  2. Execute the test script with Python 3.8 or higher with the requests package installed
    Example execution (from main directory of git clone):
    python3 -m pip install -r ./test_client/requirements.txt
    python3 ./test_client/test_client.py
  3. Successful output shows the messageID and the header JSON payload:
    {
    "messageID": " XXXXXXXXXXXXXX"
    }
  4. In the S3 bucket “<project name>-s3messagearchive-xxxxxx“, you should be able to find the payload of the original json with a key based on the date and time of the script execution, e.g.: YEAR/MONTH/DAY/HOUR/MINUTE with a file name of the messageID
  5. In a DynamoDB table named metaDataTable, you should find a record with a messageID equal to the messageID from above that contains all of the meta data related to the payload

Conclusion

This blog describes architectural patterns, messaging patterns, and data structures that support a highly reliable messaging system for large messages. The use of serverless services including Lambda functions, Step Functions, ElastiCache, DynamoDB, and S3 meet the requirements of modern audit systems to be scalable and reliable. The architecture shared in this blog post is suitable for a highly regulated environment to store and track messages that are larger than typical logging systems, records sized between 256k and 6MB. The architecture serves as a blueprint that can be extended and adapted to fit further serverless use cases.

For serverless learning resources, visit Serverless Land.

Surveillance through Push Notifications

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/surveillance-through-push-notifications.html

The Washington Post is reporting on the FBI’s increasing use of push notification data—”push tokens”—to identify people. The police can request this data from companies like Apple and Google without a warrant.

The investigative technique goes back years. Court orders that were issued in 2019 to Apple and Google demanded that the companies hand over information on accounts identified by push tokens linked to alleged supporters of the Islamic State terrorist group.

But the practice was not widely understood until December, when Sen. Ron Wyden (D-Ore.), in a letter to Attorney General Merrick Garland, said an investigation had revealed that the Justice Department had prohibited Apple and Google from discussing the technique.

[…]

Unlike normal app notifications, push alerts, as their name suggests, have the power to jolt a phone awake—a feature that makes them useful for the urgent pings of everyday use. Many apps offer push-alert functionality because it gives users a fast, battery-saving way to stay updated, and few users think twice before turning them on.

But to send that notification, Apple and Google require the apps to first create a token that tells the company how to find a user’s device. Those tokens are then saved on Apple’s and Google’s servers, out of the users’ reach.

The article discusses their use by the FBI, primarily in child sexual abuse cases. But we all know how the story goes:

“This is how any new surveillance method starts out: The government says we’re only going to use this in the most extreme cases, to stop terrorists and child predators, and everyone can get behind that,” said Cooper Quintin, a technologist at the advocacy group Electronic Frontier Foundation.

“But these things always end up rolling downhill. Maybe a state attorney general one day decides, hey, maybe I can use this to catch people having an abortion,” Quintin added. “Even if you trust the U.S. right now to use this, you might not trust a new administration to use it in a way you deem ethical.”

The Insecurity of Video Doorbells

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/the-insecurity-of-video-doorbells.html

Consumer Reports has analyzed a bunch of popular Internet-connected video doorbells. Their security is terrible.

First, these doorbells expose your home IP address and WiFi network name to the internet without encryption, potentially opening your home network to online criminals.

[…]

Anyone who can physically access one of the doorbells can take over the device—no tools or fancy hacking skills needed.

LLM Prompt Injection Worm

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/llm-prompt-injection-worm.html

Researchers have demonstrated a worm that spreads through prompt injection. Details:

In one instance, the researchers, acting as attackers, wrote an email including the adversarial text prompt, which “poisons” the database of an email assistant using retrieval-augmented generation (RAG), a way for LLMs to pull in extra data from outside its system. When the email is retrieved by the RAG, in response to a user query, and is sent to GPT-4 or Gemini Pro to create an answer, it “jailbreaks the GenAI service” and ultimately steals data from the emails, Nassi says. “The generated response containing the sensitive user data later infects new hosts when it is used to reply to an email sent to a new client and then stored in the database of the new client,” Nassi says.

In the second method, the researchers say, an image with a malicious prompt embedded makes the email assistant forward the message on to others. “By encoding the self-replicating prompt into the image, any kind of image containing spam, abuse material, or even propaganda can be forwarded further to new clients after the initial email has been sent,” Nassi says.

It’s a natural extension of prompt injection. But it’s still neat to see it actually working.

Research paper: “ComPromptMized: Unleashing Zero-click Worms that Target GenAI-Powered Applications.

Abstract: In the past year, numerous companies have incorporated Generative AI (GenAI) capabilities into new and existing applications, forming interconnected Generative AI (GenAI) ecosystems consisting of semi/fully autonomous agents powered by GenAI services. While ongoing research highlighted risks associated with the GenAI layer of agents (e.g., dialog poisoning, membership inference, prompt leaking, jailbreaking), a critical question emerges: Can attackers develop malware to exploit the GenAI component of an agent and launch cyber-attacks on the entire GenAI ecosystem?

This paper introduces Morris II, the first worm designed to target GenAI ecosystems through the use of adversarial self-replicating prompts. The study demonstrates that attackers can insert such prompts into inputs that, when processed by GenAI models, prompt the model to replicate the input as output (replication), engaging in malicious activities (payload). Additionally, these inputs compel the agent to deliver them (propagate) to new agents by exploiting the connectivity within the GenAI ecosystem. We demonstrate the application of Morris II against GenAI-powered email assistants in two use cases (spamming and exfiltrating personal data), under two settings (black-box and white-box accesses), using two types of input data (text and images). The worm is tested against three different GenAI models (Gemini Pro, ChatGPT 4.0, and LLaVA), and various factors (e.g., propagation rate, replication, malicious activity) influencing the performance of the worm are evaluated.

Friday Squid Blogging: New Extinct Species of Vampire Squid Discovered

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/friday-squid-blogging-new-extinct-species-of-vampire-squid-discovered.html

Paleontologists have discovered a 183-million-year-old species of vampire squid.

Prior research suggests that the vampyromorph lived in the shallows off an island that once existed in what is now the heart of the European mainland. The research team believes that the remarkable degree of preservation of this squid is due to unique conditions at the moment of the creature’s death. Water at the bottom of the sea where it ventured would have been poorly oxygenated, causing the creature to suffocate. In addition to killing the squid, it would have prevented other creatures from feeding on its remains, allowing it to become buried in the seafloor, wholly intact.

Research paper.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

NIST Cybersecurity Framework 2.0

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/03/nist-cybersecurity-framework-2-0.html

NIST has released version 2.0 of the Cybersecurity Framework:

The CSF 2.0, which supports implementation of the National Cybersecurity Strategy, has an expanded scope that goes beyond protecting critical infrastructure, such as hospitals and power plants, to all organizations in any sector. It also has a new focus on governance, which encompasses how organizations make and carry out informed decisions on cybersecurity strategy. The CSF’s governance component emphasizes that cybersecurity is a major source of enterprise risk that senior leaders should consider alongside others such as finance and reputation.

[…]

The framework’s core is now organized around six key functions: Identify, Protect, Detect, Respond and Recover, along with CSF 2.0’s newly added Govern function. When considered together, these functions provide a comprehensive view of the life cycle for managing cybersecurity risk.

The updated framework anticipates that organizations will come to the CSF with varying needs and degrees of experience implementing cybersecurity tools. New adopters can learn from other users’ successes and select their topic of interest from a new set of implementation examples and quick-start guides designed for specific types of users, such as small businesses, enterprise risk managers, and organizations seeking to secure their supply chains.

This is a big deal. The CSF is widely used, and has been in need of an update. And NIST is exactly the sort of respected organization to do this correctly.

Some news articles.

How the “Frontier” Became the Slogan of Uncontrolled AI

Post Syndicated from B. Schneier original https://www.schneier.com/blog/archives/2024/02/how-the-frontier-became-the-slogan-of-uncontrolled-ai.html

Artificial intelligence (AI) has been billed as the next frontier of humanity: the newly available expanse whose exploration will drive the next era of growth, wealth, and human flourishing. It’s a scary metaphor. Throughout American history, the drive for expansion and the very concept of terrain up for grabs—land grabs, gold rushes, new frontiers—have provided a permission structure for imperialism and exploitation. This could easily hold true for AI.

This isn’t the first time the concept of a frontier has been used as a metaphor for AI, or technology in general. As early as 2018, the powerful foundation models powering cutting-edge applications like chatbots have been called “frontier AI.” In previous decades, the internet itself was considered an electronic frontier. Early cyberspace pioneer John Perry Barlow wrote “Unlike previous frontiers, this one has no end.” When he and others founded the internet’s most important civil liberties organization, they called it the Electronic Frontier Foundation.

America’s experience with frontiers is fraught, to say the least. Expansion into the Western frontier and beyond has been a driving force in our country’s history and identity—and has led to some of the darkest chapters of our past. The tireless drive to conquer the frontier has directly motivated some of this nation’s most extreme episodes of racism, imperialism, violence, and exploitation.

That history has something to teach us about the material consequences we can expect from the promotion of AI today. The race to build the next great AI app is not the same as the California gold rush. But the potential that outsize profits will warp our priorities, values, and morals is, unfortunately, analogous.

Already, AI is starting to look like a colonialist enterprise. AI tools are helping the world’s largest tech companies grow their power and wealth, are spurring nationalistic competition between empires racing to capture new markets, and threaten to supercharge government surveillance and systems of apartheid. It looks more than a bit like the competition among colonialist state and corporate powers in the seventeenth century, which together carved up the globe and its peoples. By considering America’s past experience with frontiers, we can understand what AI may hold for our future, and how to avoid the worst potential outcomes.

America’s “Frontier” Problem

For 130 years, historians have used frontier expansion to explain sweeping movements in American history. Yet only for the past thirty years have we generally acknowledged its disastrous consequences.

Frederick Jackson Turner famously introduced the frontier as a central concept for understanding American history in his vastly influential 1893 essay. As he concisely wrote, “American history has been in a large degree the history of the colonization of the Great West.”

Turner used the frontier to understand all the essential facts of American life: our culture, way of government, national spirit, our position among world powers, even the “struggle” of slavery. The endless opportunity for westward expansion was a beckoning call that shaped the American way of life. Per Turner’s essay, the frontier resulted in the individualistic self-sufficiency of the settler and gave every (white) man the opportunity to attain economic and political standing through hardscrabble pioneering across dangerous terrain.The New Western History movement, gaining steam through the 1980s and led by researchers like Patricia Nelson Limerick, laid plain the racial, gender, and class dynamics that were always inherent to the frontier narrative. This movement’s story is one where frontier expansion was a tool used by the white settler to perpetuate a power advantage.The frontier was not a siren calling out to unwary settlers; it was a justification, used by one group to subjugate another. It was always a convenient, seemingly polite excuse for the powerful to take what they wanted. Turner grappled with some of the negative consequences and contradictions of the frontier ethic and how it shaped American democracy. But many of those whom he influenced did not do this; they celebrated it as a feature, not a bug. Theodore Roosevelt wrote extensively and explicitly about how the frontier and his conception of white supremacy justified expansion to points west and, through the prosecution of the Spanish-American War, far across the Pacific. Woodrow Wilson, too, celebrated the imperial loot from that conflict in 1902. Capitalist systems are “addicted to geographical expansion” and even, when they run out of geography, seek to produce new kinds of spaces to expand into. This is what the geographer David Harvey calls the “spatial fix.”Claiming that AI will be a transformative expanse on par with the Louisiana Purchase or the Pacific frontiers is a bold assertion—but increasingly plausible after a year dominated by ever more impressive demonstrations of generative AI tools. It’s a claim bolstered by billions of dollars in corporate investment, by intense interest of regulators and legislators worldwide in steering how AI is developed and used, and by the variously utopian or apocalyptic prognostications from thought leaders of all sectors trying to understand how AI will shape their sphere—and the entire world.

AI as a Permission Structure

Like the western frontier in the nineteenth century, the maniacal drive to unlock progress via advancement in AI can become a justification for political and economic expansionism and an excuse for racial oppression.

In the modern day, OpenAI famously paid dozens of Kenyans little more than a dollar an hour to process data used in training their models underlying products such as ChatGPT. Paying low wages to data labelers surely can’t be equated to the chattel slavery of nineteenth-century America. But these workers did endure brutal conditions, including being set to constantly review content with “graphic scenes of violence, self-harm, murder, rape, necrophilia, child abuse, bestiality, and incest.” There is a global market for this kind of work, which has been essential to the most important recent advances in AI such as Reinforcement Learning with Human Feedback, heralded as the most important breakthrough of ChatGPT.

The gold rush mentality associated with expansion is taken by the new frontiersmen as permission to break the rules, and to build wealth at the expense of everyone else. In 1840s California, gold miners trespassed on public lands and yet were allowed to stake private claims to the minerals they found, and even to exploit the water rights on those lands. Again today, the game is to push the boundaries on what rule-breaking society will accept, and hope that the legal system can’t keep up.

Many internet companies have behaved in exactly the same way since the dot-com boom. The prospectors of internet wealth lobbied for, or simply took of their own volition, numerous government benefits in their scramble to capture those frontier markets. For years, the Federal Trade Commission has looked the other way or been lackadaisical in halting antitrust abuses by Amazon, Facebook, and Google. Companies like Uber and Airbnb exploited loopholes in, or ignored outright, local laws on taxis and hotels. And Big Tech platforms enjoyed a liability shield that protected them from punishment the contents people posted to their sites.

We can already see this kind of boundary pushing happening with AI.

Modern frontier AI models are trained using data, often copyrighted materials, with untested legal justification. Data is like water for AI, and, like the fight over water rights in the West, we are repeating a familiar process of public acquiescence to private use of resources. While some lawsuits are pending, so far AI companies have faced no significant penalties for the unauthorized use of this data.

Pioneers of self-driving vehicles tried to skip permitting processes and used fake demonstrations of their capabilities to avoid government regulation and entice consumers. Meanwhile, AI companies’ hope is that they won’t be held to blame if the AI tools they produce spew out harmful content that causes damage in the real world. They are trying to use the same liability shield that fostered Big Tech’s exploitation of the previous electronic frontiers—the web and social media—to protect their own actions.

Even where we have concrete rules governing deleterious behavior, some hope that using AI is itself enough to skirt them. Copyright infringement is illegal if a person does it, but would that same person be punished if they train a large language model to regurgitate copyrighted works? In the political sphere, the Federal Election Commission has precious few powers to police political advertising; some wonder if they simply won’t be considered relevant if people break those rules using AI.

AI and American Exceptionalism

Like The United States’ historical frontier, AI has a feel of American exceptionalism. Historically, we believed we were different from the Old World powers of Europe because we enjoyed the manifest destiny of unrestrained expansion between the oceans. Today, we have the most CPU power, the most data scientists, the most venture-capitalist investment, and the most AI companies. This exceptionalism has historically led many Americans to believe they don’t have to play by the same rules as everyone else.

Both historically and in the modern day, this idea has led to deleterious consequences such as militaristic nationalism (leading to justifying of foreign interventions in Iraq and elsewhere), masking of severe inequity within our borders, abdication of responsibility from global treaties on climate and law enforcement, and alienation from the international community. American exceptionalism has also wrought havoc on our country’s engagement with the internet, including lawless spying and surveillance by forces like the National Security Agency.

The same line of thinking could have disastrous consequences if applied to AI. It could perpetuate a nationalistic, Cold War–style narrative about America’s inexorable struggle with China, this time predicated on an AI arms race. Moral exceptionalism justifies why we should be allowed to use tools and weapons that are dangerous in the hands of a competitor, or enemy. It could enable the next stage of growth of the military-industrial complex, with claims of an urgent need to modernize missile systems and drones through using AI. And it could renew a rationalization for violating civil liberties in the US and human rights abroad, empowered by the idea that racial profiling is more objective if enforced by computers.The inaction of Congress on AI regulation threatens to land the US in a regime of de facto American exceptionalism for AI. While the EU is about to pass its comprehensive AI Act, lobbyists in the US have muddled legislative action. While the Biden administration has used its executive authority and federal purchasing power to exert some limited control over AI, the gap left by lack of legislation leaves AI in the US looking like the Wild West—a largely unregulated frontier.The lack of restraint by the US on potentially dangerous AI technologies has a global impact. First, its tech giants let loose their products upon the global public, with the harms that this brings with it. Second, it creates a negative incentive for other jurisdictions to more forcefully regulate AI. The EU’s regulation of high-risk AI use cases begins to look like unilateral disarmament if the US does not take action itself. Why would Europe tie the hands of its tech competitors if the US refuses to do the same?

AI and Unbridled Growth

The fundamental problem with frontiers is that they seem to promise cost-free growth. There was a constant pressure for American westward expansion because a bigger, more populous country accrues more power and wealth to the elites and because, for any individual, a better life was always one more wagon ride away into “empty” terrain. AI presents the same opportunities. No matter what field you’re in or what problem you’re facing, the attractive opportunity of AI as a free labor multiplier probably seems like the solution; or, at least, makes for a good sales pitch.

That would actually be okay, except that the growth isn’t free. America’s imperial expansion displaced, harmed, and subjugated native peoples in the Americas, Africa, and the Pacific, while enlisting poor whites to participate in the scheme against their class interests. Capitalism makes growth look like the solution to all problems, even when it’s clearly not. The problem is that so many costs are externalized. Why pay a living wage to human supervisors training AI models when an outsourced gig worker will do it at a fraction of the cost? Why power data centers with renewable energy when it’s cheaper to surge energy production with fossil fuels? And why fund social protections for wage earners displaced by automation if you don’t have to? The potential of consumer applications of AI, from personal digital assistants to self-driving cars, is irresistible; who wouldn’t want a machine to take on the most routinized and aggravating tasks in your daily life? But the externalized cost for consumers is accepting the inevitability of domination by an elite who will extract every possible profit from AI services.

Controlling Our Frontier Impulses

None of these harms are inevitable. Although the structural incentives of capitalism and its growth remain the same, we can make different choices about how to confront them.

We can strengthen basic democratic protections and market regulations to avoid the worst impacts of AI colonialism. We can require ethical employment for the humans toiling to label data and train AI models. And we can set the bar higher for mitigating bias in training and harm from outputs of AI models.

We don’t have to cede all the power and decision making about AI to private actors. We can create an AI public option to provide an alternative to corporate AI. We can provide universal access to ethically built and democratically governed foundational AI models that any individual—or company—could use and build upon.

More ambitiously, we can choose not to privatize the economic gains of AI. We can cap corporate profits, raise the minimum wage, or redistribute an automation dividend as a universal basic income to let everyone share in the benefits of the AI revolution. And, if these technologies save as much labor as companies say they do, maybe we can also all have some of that time back.

And we don’t have to treat the global AI gold rush as a zero-sum game. We can emphasize international cooperation instead of competition. We can align on shared values with international partners and create a global floor for responsible regulation of AI. And we can ensure that access to AI uplifts developing economies instead of further marginalizing them.

This essay was written with Nathan Sanders, and was originally published in Jacobin.

A Cyber Insurance Backstop

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/02/a-cyber-insurance-backstop.html

In the first week of January, the pharmaceutical giant Merck quietly settled its years-long lawsuit over whether or not its property and casualty insurers would cover a $700 million claim filed after the devastating NotPetya cyberattack in 2017. The malware ultimately infected more than 40,000 of Merck’s computers, which significantly disrupted the company’s drug and vaccine production. After Merck filed its $700 million claim, the pharmaceutical giant’s insurers argued that they were not required to cover the malware’s damage because the cyberattack was widely attributed to the Russian government and therefore was excluded from standard property and casualty insurance coverage as a “hostile or warlike act.”

At the heart of the lawsuit was a crucial question: Who should pay for massive, state-sponsored cyberattacks that cause billions of dollars’ worth of damage?

One possible solution, touted by former Department of Homeland Security Secretary Michael Chertoff on a recent podcast, would be for the federal government to step in and help pay for these sorts of attacks by providing a cyber insurance backstop. A cyber insurance backstop would provide a means for insurers to receive financial support from the federal government in the event that there was a catastrophic cyberattack that caused so much financial damage that the insurers could not afford to cover all of it.

In his discussion of a potential backstop, Chertoff specifically references the Terrorism Risk Insurance Act (TRIA) as a model. TRIA was passed in 2002 to provide financial assistance to the insurers who were reeling from covering the costs of the Sept. 11, 2001, terrorist attacks. It also created the Terrorism Risk Insurance Program (TRIP), a public-private system of compensation for some terrorism insurance claims. The 9/11 attacks cost insurers and reinsurers $47 billion. It was one of the most expensive insured events in history and prompted many insurers to stop offering terrorism coverage, while others raised the premiums for such policies significantly, making them prohibitively expensive for many businesses. The government passed TRIA to provide support for insurers in the event of another terrorist attack, so that they would be willing to offer terrorism coverage again at reasonable rates. President Biden’s 2023 National Cybersecurity Strategy tasked the Treasury and Homeland Security Departments with investigating possible ways of implementing something similar for large cyberattacks.

There is a growing (and unsurprising) consensus among insurers in favor of the creation and implementation of a federal cyber insurance backstop. Like terrorist attacks, catastrophic cyberattacks are difficult for insurers to predict or model because there is not very good historical data about them—and even if there were, it’s not clear that past patterns of cyberattacks will dictate future ones. What’s more, cyberattacks could cost insurers astronomic sums of money, especially if all of their policyholders were simultaneously affected by the same attack. However, despite this consensus and the fact that this idea of the government acting as the “insurer of last resort” was first floated more than a decade ago, actually developing a sound, thorough proposal for a backstop has proved to be much more challenging than many insurers and policymakers anticipated.

One major point of issue is determining a threshold for what types of cyberattacks should trigger a backstop. Specific characteristics of cyberattacks—such as who perpetrated the attack, the motive behind it, and total damage it has caused—are often exceedingly difficult to determine. Therefore, even if policymakers could agree on what types of attacks they think the government should pay for based on these characteristics, they likely won’t be able to calculate which incursions actually qualify for assistance.

For instance, NotPetya is estimated to have caused more than $10 billion in damage worldwide, but the quantifiable amount of damage it actually did is unknown. The attack caused such a wide variety of disruptions in so many different industries, many of which likely went unreported since many companies had no incentive to publicize their security failings and were not required to do so. Observers do, however, have a pretty good idea who was behind the NotPetya attack because several governments, including the United States and the United Kingdom, issued coordinated statements blaming the Russian military. As for the motive behind NotPetya, the program was initially transmitted through Ukrainian accounting software, which suggests that it was intended to target Ukrainian critical infrastructure. But notably, this type of coordinated, consensus-based attribution to a specific government is relatively rare when it comes to cyberattacks. Future attacks are not likely to receive the same determination.

In the absence of a government backstop, the insurance industry has begun to carve out larger and larger exceptions to their standard cyber coverage. For example, in a pair of rulings against Merck’s insurers, judges in New Jersey ruled that the insurance exclusions for “hostile or warlike acts” (such as the one in Merck’s property policy that excluded coverage for “loss or damage caused by hostile or warlike action in time of peace or war … by any government or sovereign power”) were not sufficiently specific to encompass a cyberattack such as NotPetya that did not involve the use of traditional force.

Accordingly, insurers such as Lloyd’s have begun to change their policy language to explicitly exclude broad swaths of cyberattacks that are perpetrated by nation-states. In an August 2022 bulletin, Lloyd’s instructed its underwriters to exclude from all cyber insurance policies not just losses arising from war but also “losses arising from state backed cyber-attacks that (a) significantly impair the ability of a state to function or (b) that significantly impair the security capabilities of a state.”  Other insurers, such as Chubb, have tried to avoid tricky questions about attribution by suggesting a government response-based exclusion for war that only applies if a government responds to a cyberattack by authorizing the use of force. Chubb has also introduced explicit definitions for cyberattacks that pose a “systemic risk” or impact multiple entities simultaneously. But most of this language has not yet been tested by insurers trying to deny claims. No one, including the companies buying the policies with these exclusions written into them, really knows exactly which types of cyberattacks they exclude. It’s not clear what types of cyberattacks courts will recognize as being state-sponsored, or posing systemic risks, or significantly impairing the ability of a state to function. And for the policyholders’ whose insurance exclusions feature this sort of language, it matters a great deal how that language in their exclusions will be parsed and understood by courts adjudicating claim disputes.

These types of recent exclusions leave a large hole in companies’ coverage for cyber risks, placing even more pressure on the government to help. One of the reasons Chertoff gives for why the backstop is important is to help clarify for organizations what cyber risk-related costs they are and are not responsible for. That clarity will require very specific definitions of what types of cyberattacks the government will and will not pay for. And as the insurers know, it can be quite difficult to anticipate what the next catastrophic cyberattack will look like or how to craft a policy that will enable the government to pay only for a narrow slice of cyberattacks in a varied and unpredictable threat landscape. Get this wrong, and the government will end up writing some very large checks.

And in comparison to insurers’ coverage of terrorist attacks, large-scale cyberattacks are much more common and affect far more organizations, which makes it a far more costly risk that no one wants to take on. Organizations don’t want to—that’s why they buy insurance. Insurance companies don’t want to—that’s why they look to the government for assistance. But, so far, the U.S. government doesn’t want to take on the risk, either.

It is safe to assume, however, that regardless of whether a formal backstop is established, the federal government would step in and help pay for a sufficiently catastrophic cyberattack. If the electric grid went down nationwide, for instance, the U.S. government would certainly help cover the resulting costs. It’s possible to imagine any number of catastrophic scenarios in which an ad hoc backstop would be implemented hastily to help address massive costs and catastrophic damage, but that’s not primarily what insurers and their policyholders are looking for. They want some reassurance and clarity up front about what types of incidents the government will help pay for. But to provide that kind of promise in advance, the government likely would have to pair it with some security requirements, such as implementing multifactor authentication, strong encryption, or intrusion detection systems. Otherwise, they create a moral hazard problem, where companies may decide they can invest less in security knowing that the government will bail them out if they are the victims of a really expensive attack.

The U.S. government has been looking into the issue for a while, though, even before the 2023 National Cybersecurity Strategy was released. In 2022, for instance, the Federal Insurance Office in the Treasury Department published a Request for Comment on a “Potential Federal Insurance Response to Catastrophic Cyber Incidents.” The responses recommended a variety of different possible backstop models, ranging from expanding TRIP to encompass certain catastrophic cyber incidents, to creating a new structure similar to the National Flood Insurance Program that helps underwrite flood insurance, to trying a public-private partnership backstop model similar to the United Kingdom’s Pool Re program.

Many of these responses rightly noted that while it might eventually make sense to have some federal backstop, implementing such a program immediately might be premature. University of Edinburgh Professor Daniel Woods, for example, made a compelling case for why it was too soon to institute a backstop in Lawfare last year. Woods wrote,

One might argue similarly that a cyber insurance backstop would subsidize those companies whose security posture creates the potential for cyber catastrophe, such as the NotPetya attack that caused $10 billion in damage. Infection in this instance could have been prevented by basic cyber hygiene. Why should companies that do not employ basic cyber hygiene be subsidized by industry peers? The argument is even less clear for a taxpayer-funded subsidy.

The answer is to ensure that a backstop applies only to companies that follow basic cyber hygiene guidelines, or to insurers who require those hygiene measures of their policyholders. These are the types of controls many are familiar with: complicated passwords, app-based two-factor authentication, antivirus programs, and warning labels on emails. But this is easier said than done. To a surprising extent, it is difficult to know which security controls really work to improve companies’ cybersecurity. Scholars know what they think works: strong encryption, multifactor authentication, regular software updates, and automated backups. But there is not anywhere near as much empirical evidence as there ought to be about how effective these measures are in different implementations, or how much they reduce a company’s exposure to cyber risk.

This is largely due to companies’ reluctance to share detailed, quantitative information about cybersecurity incidents because any such information may be used to criticize their security posture or, even worse, as evidence for a government investigation or class-action lawsuit. And when insurers and regulators alike try to gather that data, they often run into legal roadblocks because these investigations are often run by lawyers who claim that the results are shielded by attorney-client privilege or work product doctrine. In some cases, companies don’t write down their findings at all to avoid the possibility of its being used against them in court. Without this data, it’s difficult for insurers to be confident that what they’re requiring of their policyholders will really work to improve those policyholders’ security and decrease their claims for cybersecurity-related incidents under their policies. Similarly, it’s hard for the federal government to be confident that they can impose requirements for a backstop that will actually raise the level of cybersecurity hygiene nationwide.

The key to managing cyber risks—both large and small—and designing a cyber backstop is determining what security practices can effectively mitigate the impact of these attacks. If there were data showing which controls work, insurers could then require that their policyholders use them, in the same way they require policyholders to install smoke detectors or burglar alarms. Similarly, if the government had better data about which security tools actually work, it could establish a backstop that applied only to victims who have used those tools as safeguards. The goal of this effort, of course, is to improve organizations’ overall cybersecurity in addition to providing financial assistance.

There are a number of ways this data could be collected. Insurers could do it through their claims databases and then aggregate that data across carriers to policymakers. They did this for car safety measures starting in the 1950s, when a group of insurance associations founded the Insurance Institute for Highway Safety. The government could use its increasing reporting authorities, for instance under the Cyber Incident Reporting for Critical Infrastructure Act of 2022, to require that companies report data about cybersecurity incidents, including which countermeasures were in place and the root causes of the incidents. Or the government could establish an entirely new entity in the form of a Bureau for Cyber Statistics that would be devoted to collecting and analyzing this type of data.

Scholars and policymakers can’t design a cyber backstop until this data is collected and studied to determine what works best for cybersecurity. More broadly, organizations’ cybersecurity cannot improve until more is known about the threat landscape and the most effective tools for managing cyber risk.

If the cybersecurity community doesn’t pause to gather that data first, then it will never be able to meaningfully strengthen companies’ security postures against large-scale cyberattacks, and insurers and government officials will just keep passing the buck back and forth, while the victims are left to pay for those attacks themselves.

This essay was written with Josephine Wolff, and was originally published in Lawfare.

China Surveillance Company Hacked

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/02/china-surveillance-company-hacked.html

Last week, someone posted something like 570 files, images and chat logs from a Chinese company called I-Soon. I-Soon sells hacking and espionage services to Chinese national and local government.

Lots of details in the news articles.

These aren’t details about the tools or techniques, more the inner workings of the company. And they seem to primarily be hacking regionally.

Keeping Remote Teams Connected: The Zabbix Advantage

Post Syndicated from Michael Kammer original https://blog.zabbix.com/keeping-remote-teams-connected-the-zabbix-advantage/27551/

The popularity of remote teams may have exploded in popularity during the COVID-19 pandemic, but it’s not a phenomenon that’s likely to trend downward anytime soon. High-profile organizations like 3M, Dropbox, Shopify, and LinkedIn are continuing to enthusiastically embrace remote working, essentially making it the “default setting” for their employees.

The shift toward remote working is not without its challenges, however. Organizations of all sizes often have little time to set up the kind of networking infrastructure and efficient processes that make sure remote workers are just as connected and productive as their on-site counterparts. In this article, we’ll take a quick look at some of the most important network monitoring challenges that remote teams face and show how Zabbix can help you tackle them as efficiently as possible.

Infrastructure and connectivity issues

A remote network is essentially a grouping of multiple smaller network setups, each with their own set of variables that can affect performance. The differences between network system and infrastructure quality at different remote destinations can often lead to low overall network performance, which in turn makes it a challenge to provide the kind of high-speed communication needed to run the remote automation tools and software applications used by remote employees and teams.

By providing straightforward and easy-to-understand visibility into a network’s connected devices and how data moves between them, Zabbix makes it easy to automatically compare data and identify any drop in network performance.

With Zabbix, you can easily keep an eye on network routers and switches, especially internet provider and uplink ports up/down. You can also monitor network latency, the error rate on ports, the packet loss to important devices, and network utilization on important ports with net.if.in/net.if.out. Here are some example triggers:

High Network Utilization: avg(/Router ABC/net.if.in[eth0],5m)>80MB
High Packet Loss: avg(/Router ABC/icmppingloss,5m)>5
High Latency: avg(/Router ABC/icmppingsec,5m)>0.1

What’s more, Zabbix allows you to create network maps with important network devices and real-time data, as well as dashboards with maps and single item/gauge widgets, all of which makes it far easier to achieve the uninterrupted connectivity that remote teams depend on.

Staying safe

Remote locations aren’t islands that can be completely isolated from external traffic. Staying vigilant and doing everything possible to eliminate data breaches is important, and taking advantage of strong encryption methods, network scanning tools, and firewalls to protect your systems is a good start. However, using a whole suite of tools to protect security can add more difficulty when it comes to integrating and monitoring them.

With Zabbix, you can count on enterprise-grade security, including encrypted communication between components, a flexible user permission schema that can be easily applied to a distributed environment, and custom user roles with a granular set of permissions for different types of users.

Zabbix also provides native support for HTTP, LDAP, and SAML authentication (which gives you an additional layer of security and improves your user experience while working with Zabbix), the ability to restrict access to sensitive information by limiting which metrics can be collected in your environment, and the ability to track changes in your environment by utilizing the Audit log. It’s all designed to make sure that there are no compromises on the security of your data when you decide to go remote.

Scalability

As a remote organization grows and its distributed systems expand, a good monitoring solution needs to be able to grow along with it in order to prevent gaps in coverage while maintaining performance and reliability. Zabbix gives you limitless scalability in the form of Zabbix proxies, which act as independent intermediaries that collect performance and availability data on behalf of a Zabbix server. You can roll out new proxies as fast as you need them, and because Zabbix is free and open source, you don’t have to worry about additional licensing costs.

Zabbix proxies allow you to see at a glance what resources are being used on your network at any given moment, which is especially handy if, like most remote teams, you have tens or even hundreds of servers and network appliances to monitor. You can also execute remote commands in remote locations – either on the proxies themselves or on the agents monitored by the proxy, and multiple frontends can be deployed for load balancing as well as for improved security and connectivity. Proxy docker containers and cloud options are available as well, enhancing flexibility and making Zabbix ideal for any organization that spans the globe (or aspires to).

Managing multiple solutions

The legacy software and systems you use were most likely designed to work in a traditional networking model. Remote working, as we’ve seen, presents a whole new range of challenges when it comes to compatibility and support.

We’ve created Zabbix to be as easy as possible to integrate with existing systems. You can easily monitor any operating system, cloud service, IP telephony service, docker container, or web server/database backend. We provide out-of-the-box monitoring for the world’s leading hardware and software vendors, and our extensively documented API makes it easy to create workflows and integrate with other systems. In addition, you can also integrate Zabbix with the most popular helpdesk, messaging, and ITSM systems, such as Slack, Jira, MS Teams, and many others.

Not only that, Zabbix is designed to serve as the ideal monitoring solution for multi-tenant environments. It serves as a single pane of glass for your entire infrastructure, and it’s easy to visualize everything that’s happening with your network with unique maps, dashboards, and templates.

Conclusion

The days of large teams all working together under the same roof are a thing of the past – the remote working trend will only accelerate as technology improves and employees get more accustomed to working with colleagues across multiple locations. That’s why it’s of paramount importance to make sure your monitoring solution has the built-in flexibility and scalability to grow with your team and your business.

If you want to see for yourself how Zabbix can help you effectively monitor a globally distributed network, contact us.

 

 

The post Keeping Remote Teams Connected: The Zabbix Advantage appeared first on Zabbix Blog.

Apple Announces Post-Quantum Encryption Algorithms for iMessage

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/02/apple-announces-post-quantum-encryption-algorithms-for-imessage.html

Apple announced PQ3, its post-quantum encryption standard based on the Kyber secure key-encapsulation protocol, one of the post-quantum algorithms selected by NIST in 2022.

There’s a lot of detail in the Apple blog post, and more in Douglas Stabila’s security analysis.

I am of two minds about this. On the one hand, it’s probably premature to switch to any particular post-quantum algorithms. The mathematics of cryptanalysis for these lattice and other systems is still rapidly evolving, and we’re likely to break more of them—and learn a lot in the process—over the coming few years. But if you’re going to make the switch, this is an excellent choice. And Apple’s ability to do this so efficiently speaks well about its algorithmic agility, which is probably more important than its particular cryptographic design. And it is probably about the right time to worry about, and defend against, attackers who are storing encrypted messages in hopes of breaking them later on future quantum computers.

Friday Squid Blogging: Illex Squid and Climate Change

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/02/friday-squid-blogging-illex-squid-and-climate-change.html

There are correlations between the populations of the Illex Argentines squid and water temperatures.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

AIs Hacking Websites

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/02/ais-hacking-websites.html

New research:

LLM Agents can Autonomously Hack Websites

Abstract: In recent years, large language models (LLMs) have become increasingly capable and can now interact with tools (i.e., call functions), read documents, and recursively call themselves. As a result, these LLMs can now function autonomously as agents. With the rise in capabilities of these agents, recent work has speculated on how LLM agents would affect cybersecurity. However, not much is known about the offensive capabilities of LLM agents.

In this work, we show that LLM agents can autonomously hack websites, performing tasks as complex as blind database schema extraction and SQL injections without human feedback. Importantly, the agent does not need to know the vulnerability beforehand. This capability is uniquely enabled by frontier models that are highly capable of tool use and leveraging extended context. Namely, we show that GPT-4 is capable of such hacks, but existing open-source models are not. Finally, we show that GPT-4 is capable of autonomously finding vulnerabilities in websites in the wild. Our findings raise questions about the widespread deployment of LLMs.

New Image/Video Prompt Injection Attacks

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/02/new-image-video-prompt-injection-attacks.html

Simon Willison has been playing with the video processing capabilities of the new Gemini Pro 1.5 model from Google, and it’s really impressive.

Which means a lot of scary new video prompt injection attacks. And remember, given the current state of technology, prompt injection attacks are impossible to prevent in general.

Details of a Phone Scam

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/02/details-of-a-phone-scam.html

First-person account of someone who fell for a scam, that started as a fake Amazon service rep and ended with a fake CIA agent, and lost $50,000 cash. And this is not a naive or stupid person.

The details are fascinating. And if you think it couldn’t happen to you, think again. Given the right set of circumstances, it can.

It happened to Cory Doctorow.

EDITED TO ADD (2/23): More scams, these involving timeshares.