Other industry solutions require a photographer or institution to vouch for an image using their own credentials. We are concerned this puts some photographers, such as those operating in conflict zones, in a difficult position; it should not be necessary to forgo anonymity in order to prove image authenticity. We built Apple Reference Image to avoid using an explicit, public credential for photographers, and to avoid even implicit public association between different photos taken by the same sensor. The final reference image is instead signed by Apple’s signing service, after validation by PCC. That signature is backed by Apple’s strongest technical guarantees...

A few of the highlights:

• AI agents increasingly handled reconnaissance, exploitation, data theft, propaganda production, surveillance workflows, and research while humans selected targets, set goals, and reviewed important outputs.
• The report describes attackers using AI to industrialize credential theft, cloud compromise, phishing, vulnerability research, and the extraction of sensitive data from downstream organizations. ...

We identified a cell of threat actors based in northern Yemen running three weapons development programs: a guided rocket that used a commodity phone-class flight computer with final-phase homing guidance; a multi-stage ballistic missile with a stated range goal above 2,000 km; and a multi-variant missile (referred to as the “R2000” set) that included a hypersonic glide vehicle variant...

On the IPI benchmark, Opus 5 improved over Opus 4.8, reducing the probability of an attacker succeeding within 15 attempts from 5.5% to 2.0%, and from 0.5% to 0.2% on 1 attempt. It also improved on Sonnet 5 (5.9% at k=15) and Mythos 5 (2.6%), making it the most robust model evaluated. Opus 5 also outperformed all non-Claude models on this benchmark. The most robust non-Claude model was Muse Spark at 16.5% within 15 attempts—more than eight times Opus 5’s rate. The most capable GPT 5.6 variant, Sol, was comparable to its predecessor GPT 5.5 (20.0% versus 20.8% within 15 attempts), and was 10 times as likely to be successfully attacked as Claude Opus 5 at 2.0%. The other GPT 5.6 variants are less robust, at 30.4% (Terra) and 43.9% (Luna). A single attempt against GPT 5.6 Sol succeeded 3.1% of the time, higher than the 2.0% an attacker achieved against Opus 5 after fifteen attempts...

Details:

The _index.js payload begins with a large JavaScript block comment containing fake system instructions and policy-triggering content. Because it is inside a comment, it does not affect JavaScript execution. The runtime skips it. The real malware begins after the comment with a try{eval(…)} wrapper around a large character-code array and a ROT-style substitution function.

This header appears designed for AI-mediated analysis, not for Node, Bun, or Python. It attempts to derail scanners or analyst copilots that feed the beginning of a file to a language model without clearly isolating the content as untrusted data. In weak pipelines, this can cause refusal behavior, prompt confusion, context pollution, or premature classification before the scanner reaches the actual malware...

Executive Summary: Let’s say you wanted to make sure that your AI is secure. Can you just maximize the security and privacy benchmark and call it a day? Nope, because benchmarks don’t actually work for measuring AI capabilities (even when they are NOT emergent systemic properties like security). So let’s take a step back: how do you measure security in the first place? Good question. Over the last 30 years, security engineering for software evolved from black box penetration testing, through whitebox code analysis and architectural risk analysis to de facto process-driven standards like the Building Security In Maturity Model (BSIMM). Software had a very deep impact on business operations, and it appears that AI is going to have an even deeper impact. Will a software security-like measurement move work for AI? Probably. In the meantime we can make real progress in AI security by cleaning up our WHAT piles and managing risk by identifying and applying good assurance processes. (Spoiler alert: no matter what we do, we still don’t get a security meter for AI, so we need to be extra vigilant about security.)...

There are strong noticeable patterns among these 50 passwords that can be seen easily:

• All of the passwords start with a letter, usually uppercase G, almost always followed by the digit 7.
• Character choices are highly uneven ­ for example, L , 9, m, 2, $ and # appeared in all 50 passwords, but 5 and @ only appeared in one password each, and most of the letters in the alphabet never appeared at all.
• There are no repeating characters within any password. Probabilistically, this would be very unlikely if the passwords were truly random ­ but Claude preferred to avoid repeating characters, possibly because it “looks like it’s less random”. ...

China is already the world’s largest exporter of AI powered surveillance technology; new surveillance technologies and platforms developed in China are also not likely to simply stay there. By exposing the full scope of China’s AI driven control apparatus, this report presents clear, evidence based insights for policymakers, civil society, the media and technology companies seeking to counter the rise of AI enabled repression and human rights violations, and China’s growing efforts to project that repression beyond its borders...

The administration has also adapted its methods of social media surveillance. Though agencies like the State Department have gathered millions of handles and monitored political discussions online, the Trump administration has been more explicit in who it’s targeting. Secretary of State Marco Rubio announced a new, zero-tolerance “Catch and Revoke” strategy, which uses AI to monitor the public speech of foreign nationals and revoke visas...

This primer maps what we currently know about generative AI’s role in scams, the communities most at risk, and the broader economic and cultural shifts that are making people more willing to take risks, more vulnerable to deception, and more likely to either perpetuate scams or fall victim to them.

AI-enhanced scams are not merely financial or technological crimes; they also exploit social vulnerabilities ­ whether short-term, like travel, or structural, like precarious employment. This means they require social solutions in addition to technical ones. By examining how scammers are changing and accelerating their methods, we hope to show that defending against them will require a constellation of cultural shifts, corporate interventions, and eff­ective legislation...

Too much good detail to summarize, but here are two items:

First, the authors found that the number of US-based investors in spyware has notably increased in the past year, when compared with the sample size of the spyware market captured in the first Mythical Beasts project. In the first edition, the United States was the second-largest investor in the spyware market, following Israel. In that edition, twelve investors were observed to be domiciled within the United States—­whereas in this second edition, twenty new US-based investors were observed investing in the spyware industry in 2024. This indicates a significant increase of US-based investments in spyware in 2024, catapulting the United States to being the largest investor in this sample of the spyware market. This is significant in scale, as US-based investment from 2023 to 2024 largely outpaced that of other major investing countries observed in the first dataset, including Italy, Israel, and the United Kingdom. It is also significant in the disparity it points to ­the visible enforcement gap between the flow of US dollars and US policy initiatives. Despite numerous US policy actions, such as the addition of spyware vendors on the ...

I’m asked this all the time, and I can only ever give a qualitative hand-wavy answer. But Jason Healey and Tarang Jain’s latest Lawfare piece has amassed data.

The essay provides the first framework for metrics about how we are all doing collectively—and not just how an individual network is doing. Healey wrote to me in email:

The work rests on three key insights: (1) defenders need a framework (based in threat, vulnerability, and consequence) to categorize the flood of potentially relevant security metrics; (2) trends are what matter, not specifics; and (3) to start, we should avoid getting bogged down in collecting data and just use what’s already being reported by amazing teams at Verizon, Cyentia, Mandiant, IBM, FBI, and so many others...

A hacker working for the Sinaloa drug cartel was able to obtain an FBI official’s phone records and use Mexico City’s surveillance cameras to help track and kill the agency’s informants in 2018, according to a new US justice department report.

The incident was disclosed in a justice department inspector general’s audit of the FBI’s efforts to mitigate the effects of “ubiquitous technical surveillance,” a term used to describe the global proliferation of cameras and the thriving trade in vast stores of communications, travel, and location data...

A new study found that many commercials VPNS are (often surreptitiously) owned by Chinese companies.

It would be hard for U.S. users to avoid the Chinese VPNs. The ownership of many appeared deliberately opaque, with several concealing their structure behind layers of offshore shell companies. TTP was able to determine the Chinese ownership of the 20 VPN apps being offered to Apple’s U.S. users by piecing together corporate documents from around the world. None of those apps clearly disclosed their Chinese ownership...

It’s full of good advice. I especially appreciate this warning:

When deciding whether to use Advanced Cryptography, start with a clear articulation of the problem, and use that to guide the development of an appropriate solution. That is, you should not start with an Advanced Cryptography technique, and then attempt to fit the functionality it provides to the problem. ...