Is Claude Mythos “Terrifying”? | AI Reality Check
Episode
24 min
Read time
2 min
Topics
Fundraising & VC, Marketing, Artificial Intelligence
AI-Generated Summary
Key Takeaways
- ✓LLM cybersecurity baseline: Security researchers have used LLMs to exploit vulnerabilities since GPT-4, which successfully exploited 87% of presented vulnerabilities in a 2024 IBM study. Anthropic's own earlier Opus 4.6 model already identified over 500 exploitable zero-day vulnerabilities. Mythos did not introduce a new capability category — it continues a three-to-four-year-old trend.
- ✓Independent replication test: Researchers from Hugging Face tested the specific vulnerabilities Anthropic highlighted in the Mythos announcement against small, cheap open-weight models. Eight out of eight models — including one with only 3.6 billion parameters costing 11 cents per million tokens — detected the same flagship FreeBSD exploit Anthropic used as its headline example.
- ✓AISI benchmark results: The UK AI Security Institute tested Mythos directly on capture-the-flag security tasks. Performance clustered near GPT-5 and Opus 4.6, with no disproportionate jump. On a contrived 32-step attack scenario, Mythos completed 22 steps on average versus Opus 4.6's 16 — a measurable but incremental gain, not a capability threshold crossing.
- ✓Agent tuning vs. model intelligence: Improvements in LLM exploitation benchmarks may reflect better agent compatibility rather than deeper cybersecurity understanding. Because models require external agents to execute multi-step attacks, recent performance gains could stem from companies tuning models to follow longer instruction chains for coding agents — a separate commercial priority unrelated to security reasoning.
- ✓Marketing vs. capability gap: When evaluating AI announcements, cross-reference company claims against independent researcher replication tests before drawing conclusions. Anthropic briefed government officials and journalists directly, generating Thomas Friedman-level alarm. Previous model releases showing comparable benchmark jumps received no equivalent coverage, revealing that narrative framing — not capability magnitude — drove the reaction.
What It Covers
Cal Newport analyzes whether Claude Mythos, Anthropic's newest AI model, represents a genuine cybersecurity breakthrough. Using independent security researcher findings and UK AI Security Institute benchmark data, Newport argues the model's capabilities show incremental improvement over existing models, not the paradigm-shifting threat Anthropic's marketing campaign suggested.
Key Questions Answered
- •LLM cybersecurity baseline: Security researchers have used LLMs to exploit vulnerabilities since GPT-4, which successfully exploited 87% of presented vulnerabilities in a 2024 IBM study. Anthropic's own earlier Opus 4.6 model already identified over 500 exploitable zero-day vulnerabilities. Mythos did not introduce a new capability category — it continues a three-to-four-year-old trend.
- •Independent replication test: Researchers from Hugging Face tested the specific vulnerabilities Anthropic highlighted in the Mythos announcement against small, cheap open-weight models. Eight out of eight models — including one with only 3.6 billion parameters costing 11 cents per million tokens — detected the same flagship FreeBSD exploit Anthropic used as its headline example.
- •AISI benchmark results: The UK AI Security Institute tested Mythos directly on capture-the-flag security tasks. Performance clustered near GPT-5 and Opus 4.6, with no disproportionate jump. On a contrived 32-step attack scenario, Mythos completed 22 steps on average versus Opus 4.6's 16 — a measurable but incremental gain, not a capability threshold crossing.
- •Agent tuning vs. model intelligence: Improvements in LLM exploitation benchmarks may reflect better agent compatibility rather than deeper cybersecurity understanding. Because models require external agents to execute multi-step attacks, recent performance gains could stem from companies tuning models to follow longer instruction chains for coding agents — a separate commercial priority unrelated to security reasoning.
- •Marketing vs. capability gap: When evaluating AI announcements, cross-reference company claims against independent researcher replication tests before drawing conclusions. Anthropic briefed government officials and journalists directly, generating Thomas Friedman-level alarm. Previous model releases showing comparable benchmark jumps received no equivalent coverage, revealing that narrative framing — not capability magnitude — drove the reaction.
Notable Moment
Shortly after Anthropic promoted Mythos as a cybersecurity breakthrough too dangerous to release publicly, security researchers discovered significant vulnerabilities in Anthropic's own leaked Claude Code source code — suggesting the company had not run its internal codebase through the model it was warning the world about.
Episode Transcript
Anthropic recently announced a new LLM named Claude Mythos. They claimed it was so good at finding and exploiting security vulnerabilities and source code that they couldn't release it to the general public for fear that our infrastructure as we know it would be hacked and collapsed. Now, as I'm sure Anthropic hoped, this announcement generated a lot of attention. Here's what Thomas Friedman said in his widely read New York Times column. Normally, right now, I would be writing about the geopolitical implications of the war with Iran, but I wanna interrupt that thought to highlight a stunning advance in artificial intelligence, one that arrives sooner than expected and that will have equally profound geopolitical implication. Friedman then goes on to conclude, and I'm quoting him here, holy cow, super intelligent AI is arriving faster than anticipated. Basically, the mood of much of the Internet right now about Claude Mythos is that Anthropic just invented the Whopper supercomputer from the 1983 Matthew Broderick movie, War Games. Well, the Whopper spends all its time thinking about World War three. Twenty four hours a day, three hundred and sixty five days a year, plays an endless series of war games using all available information on the state of the world. But here's the key question. How much of this is actually true? Well, it's Thursday, which means it's time for an AI reality check episode, so this is the perfect opportunity to look closer at these claims. Now here's my plan. I went out and read basically every independent test or assessment that I could find about Mythos and or its reported capabilities. I read all these reports, so you don't have to. And I'm going to bring out of all of this reading the key observations that you need to know. The reality, as you'll soon learn, is not nearly as simple as the ghost story that Anthropic is trying to convince us to believe. All Alright. We have a lot to cover in this episode, so let's get into it. As always, I'm Cal Newport, and this is Deep Questions, the show for people seeking depth in a distracted world. And we'll get started right after the music. Alright. So what's really going on with Claude Mythos? Well, at the core of the concerns surrounding Mythos, if you talk to an average, nontechnical person who's been following this story, they will say, here's how they understand it. That when, Anthropic trained up this new model, it displayed a new cybersecurity capability that surprised them. Oh my god. This thing can find vulnerabilities and attack systems right now, causing Anthropic then to have to hastily pull back their plan to release the model to the public. That's how most people understand this story. But that narrative is not correct. Security researchers have been using LLMs to find security vulnerabilities and program exploits since basically the beginning of consumer LLMs. This is not a new capability that emerged in Claude Mythos. …
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Books, tools, and gear mentioned in this episode
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Tools
- Claude MythosBy guest
by Anthropic
“Cal Newport analyzes whether Claude Mythos, Anthropic's newest AI model, represents a genuine cybersecurity breakthrough.”
- GPT-4By guest
by OpenAI
“Security researchers have used LLMs to exploit vulnerabilities since GPT-4, which successfully exploited 87% of presented vulnerabilities in a 2024 IBM study.”
- Opus 4.6By guest
by Anthropic
“Anthropic's own earlier Opus 4.6 model already identified over 500 exploitable zero-day vulnerabilities.”
by Hugging Face
“Researchers from Hugging Face tested the specific vulnerabilities Anthropic highlighted in the Mythos announcement against small, cheap open-weight models.”
- GPT-5By guest
by OpenAI
“The UK AI Security Institute tested Mythos directly on capture-the-flag security tasks. Performance clustered near GPT-5 and Opus 4.6.”
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