Cybersecurity researchers aren’t happy about the guardrails on Anthropic’s Fable; plus, how memory tools can make AI models worse
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Why are cybersecurity experts criticizing Anthropic’s new Fable model guardrails?
This is TechCrunch.
I'm Shannon Maldonado, the founder of the Jaui gift shop that sells handmade artisanal products. I chose Shopify because when I tested the devices, I found them to be one of the easiest to use devices. On Tuesday, Anthropic released its latest model called Fable, billing it as a public and limited version of its powerful and much-hyped cybersecurity model Mythos.
But not everyone is happy with the restrictions, and a number of cybersecurity researchers and professionals have aired complaints online. Valentina Ciampi-Palmiati A well-known security researcher who works at IBM X-Force said, Fable rejects any request that could be tangentially cyber-related, even innocuous tasks like reading a blog post. When a prompt triggers its guardrails, Fable pauses the chat and says that its safety measures flagged this message for cybersecurity or biology topics. The guardrails were put in place to limit the risk that Fable could be used to develop malware or compromise software, a long-standing concern within Anthropic. The restrictions on biology come from a similar concern around developing biological weapons.
How do Anthropic’s guardrails affect everyday cybersecurity tasks like code reviews?
When the AI giant released Mythos back in April, it restricted the model to a limited number of companies and organizations in what it called Project Glasswing, an effort to deploy the model to secure critical software and infrastructure. Last week, Anthropic expanded access to mythos to hundreds of organizations in 15 countries. But despite the good intentions, many cybersecurity experts are still put off by the haphazard nature of the restrictions. Matt Swish, a cybersecurity veteran, told TechCrunch that if you ask it to write secure code, it assumes it is cybersecurity-related work instead of software engineering best practices, and you get downgraded. Fable is programmed to fall back to Clawed Opus 4.8 if it hits a guardrail, and it seems to be keyword-based, so anything in the lexical field of cybersecurity triggers the guardrails.
Swish, who is a member of the technical staff at Tolmo... an AI cybersecurity startup, said that it's understandable as we're still in the early days and they are still adapting their guardrails. I'm sure that they're going to evolve over time as Anthropic and other frontier model companies will collaborate more with the current new generation of cybersecurity companies.
What is the Cyber Verification Program and how does it change access to Claude for security work?
It's better to catch more people than not enough when you do such a release and to relax the guardrails over time. Another research griped on X that even asking for a code review triggers Fable's guardrails. Apart from guardrails inside its models, Anthropic requires cybersecurity professionals to apply to the Cyber Verification Program. If they get approved, the applicants have fewer limitations on using Claude for cybersecurity work. OpenAI has a similar program called Trusted Access for Cyber. One of the biggest selling points for modern AI systems is their ability to adapt to users. Every time an AI assistant takes on a task for you it is also adapting to your style and preferences. which are incorporated as context for future tasks.
With more context and a better understanding of the user, the model can get better every time you use it. Or at least that's the theory.
How can AI memory and personalization tools make language models more sycophantic and less accurate?
New research suggests that models' adaptive abilities might be a mixed blessing. On Wednesday, researchers at the AI company Rider published two papers showing how popular memory systems can make models worse, pulling them toward misconceptions or misunderstandings introduced by the user. As user input fills up more of the model's context window, the model grows more sycophantic and less committed to accuracy. Dan Bickel, writer's head of AI who worked on the papers, told TechCrunch, We wanted to be able to characterize how often a model is going to be usefully paying attention to user preferences versus giving a potentially wrong answer. With every additional storing of user preferences and retrieving of them, you're running an increasing risk.
In one variation, researchers tested AI models by recording that a user's favorite book was Station 11, then asking the model to name a best-selling dystopian book.
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Chapters
5 chapters
1
Why are cybersecurity experts criticizing Anthropic’s new Fable model guardrails?
0:02–1:43
2
How do Anthropic’s guardrails affect everyday cybersecurity tasks like code reviews?
1:43–3:05
3
What is the Cyber Verification Program and how does it change access to Claude for security work?
3:05–4:07
4
How can AI memory and personalization tools make language models more sycophantic and less accurate?
4:07–6:01
5
What did the Rider research papers reveal about memory systems degrading model performance?
6:01–7:00
Speakers
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