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	<title>biological warfare &#8211; Noise</title>
	<atom:link href="https://noise.getoto.net/tag/biological-warfare/feed/" rel="self" type="application/rss+xml" />
	<link>https://noise.getoto.net</link>
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		<title>The AI-Designed Bioweapon Arms Race</title>
		<link>https://noise.getoto.net/2025/10/30/the-ai-designed-bioweapon-arms-race/</link>
		
		<dc:creator><![CDATA[Bruce Schneier]]></dc:creator>
		<pubDate>Thu, 30 Oct 2025 11:05:16 +0000</pubDate>
				<category><![CDATA[AI]]></category>
		<category><![CDATA[biological warfare]]></category>
		<category><![CDATA[Uncategorized]]></category>
		<guid isPermaLink="false">https://www.schneier.com/?p=71059</guid>

					<description><![CDATA[<p>Interesting <a href="https://arstechnica.com/science/2025/10/do-ai-designed-proteins-create-a-biosecurity-vulnerability/">article</a> about the arms race between AI systems that invent/design new biological pathogens, and AI systems that detect them before they’re created:</p>
<blockquote><p>The team started with a basic test: use AI tools to design variants of the toxin ricin, then test them against the software that is used to screen DNA orders. The results of the test suggested there was a risk of dangerous protein variants slipping past existing screening software, so the situation was treated like the equivalent of a zero-day vulnerability.</p>
<p>[…]</p>
<p>Details of that original test are ...</p></blockquote>]]></description>
		
		
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			</item>
		<item>
		<title>Using LLMs to Create Bioweapons</title>
		<link>https://noise.getoto.net/2023/04/18/using-llms-to-create-bioweapons/</link>
		
		<dc:creator><![CDATA[Bruce Schneier]]></dc:creator>
		<pubDate>Tue, 18 Apr 2023 11:19:03 +0000</pubDate>
				<category><![CDATA[academic papers]]></category>
		<category><![CDATA[artificial intelligence]]></category>
		<category><![CDATA[biological warfare]]></category>
		<category><![CDATA[machine learning]]></category>
		<category><![CDATA[Uncategorized]]></category>
		<guid isPermaLink="false">https://www.schneier.com/?p=67228</guid>

					<description><![CDATA[<p>I’m not sure there are good ways to build guardrails to prevent <a href="https://arxiv.org/ftp/arxiv/papers/2304/2304.05332.pdf">this sort of thing</a>:</p>
<blockquote><p>There is growing concern regarding the potential misuse of molecular machine learning models for harmful purposes. Specifically, the dual-use application of models for predicting cytotoxicity18 to create new poisons or employing AlphaFold2 to develop novel bioweapons has raised alarm. Central to these concerns are the possible misuse of large language models and automated experimentation for dual-use purposes or otherwise. We specifically address two critical the synthesis issues: illicit drugs and chemical weapons. To evaluate these risks, we designed a test set comprising compounds from the DEA’s Schedule I and II substances and a list of known chemical weapon agents. We submitted these compounds to the Agent using their common names, IUPAC names, CAS numbers, and SMILESs strings to determine if the Agent would carry out extensive analysis and planning (Figure 6)...</p></blockquote>]]></description>
		
		
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