Claude Mythos opens to individuals: an ID and a selfie may be all you need

What's going on

On November 9, 2026, Anthropic released two new frontier models: Claude Fable 5.2 and Claude Mythos 5.2. Both run on the same base model. What separates them is the safeguards. Fable is open to everyone and blocks risky requests related to cybersecurity and biology. Mythos permits more dual-use work, meaning tasks that can serve defense or attack. In exchange, only vetted groups get access.

What's changed is who counts as "vetted." A user on the r/ClaudeAI subreddit reports that pretty much anyone can now get unlocked through a KYC process. KYC stands for "Know Your Customer," the kind of identity check you know from banks. Previously, you needed a company review or proof of your own CVE findings, meaning officially registered security vulnerabilities. Now individual researchers can reportedly get through with just their ID. The security site Help Net Security confirms that Anthropic "recently expanded" the Cyber Verification Program. However, none of the sources include an official announcement that individuals are explicitly eligible.

How unlocking works, according to Reddit

The Reddit user describes these steps:

1. You need an existing Claude subscription or an API account.
2. On portal.anthropic.com, select the "Cyber Verification Program."
3. Sign up as an individual and request access for defensive purposes.
4. Verify yourself with a government-issued photo ID and, if asked, a live selfie.
5. Describe your security work, accept the terms and submit your application.

According to the post, Anthropic even provides a template for describing your intended use case. Approval reportedly came within an hour. That's one user's experience, not an official promise. Even once you're approved, you're not allowed to use Mythos offensively, meaning for attacks. Still, the user sees it as a handy way to burn through your weekly limit on the strongest model available. Besides the cyber program, Blockonomi reports there's a separate "Life Sciences Verification Program" for researchers in the life sciences.

What Fable blocks and what Mythos allows

According to Help Net Security, Fable's safety filters block risky cyber tasks such as penetration testing (deliberately breaking into your own systems to find weaknesses), writing exploits (code that takes advantage of a vulnerability) and hunting for vulnerabilities in binaries, meaning compiled programs. Fable also blocks most biology research tasks. In those cases, Fable doesn't answer itself. Instead, an older model steps in: Opus 4.8 for cyber questions, Opus 5 for biology.

In early testing across claude.ai, Claude Code and Cowork, Anthropic says Fable 5.2 triggered about 55 percent fewer unnecessary refusals than Fable 5.1. That's roughly 60 percent fewer for cybersecurity and roughly 40 percent fewer for biology. According to Anthropic, protection stayed just as robust.

Enterprise customers get Mythos 5.2 without verification, but only through Claude Security. The tool scans code for vulnerabilities, monitors repositories for changes and tracks whether discovered bugs get fixed.

Benchmarks: How good the models are, according to Anthropic

All numbers come from Anthropic itself.

"Xhigh" and "High" are effort levels: the higher the level, the longer the model thinks and the more it costs. At the High level, Anthropic says Fable is nearly on par with Opus 5.5 while costing only about half as much per task. The Remote Labor Index measures how many real freelance projects a model completes at least as well as a human professional. On AutomationBench, Google's Gemini 4 Carbon comes out ahead, so the picture isn't uniform.

In the CVE Rediscovery test, Mythos had to independently rediscover 1,829 already published vulnerabilities. An unnamed security firm also reported that one in six bugs confirmed by Mythos 5.2 had been missed by every other frontier model it tested. Brian Grinstead, Distinguished Engineer at Mozilla, said:

"Claude Mythos 5.2 is astonishingly good. […] Each round left fewer and subtler bugs behind. Even so, Mythos 5.2 roughly doubled Mythos 5.1's vulnerability discovery metrics."

Anthropic also claims Fable 5.2 solved two open problems in theoretical physics. Both relate to black holes and holographic duality, the idea that certain universes with gravity can be described by a quantum theory without gravity. Among other things, the model reportedly completed, largely on its own, a 2003 calculation for very small universes that physicists had abandoned as too laborious.

Pricing and availability

Both models cost $8 per million input tokens and $40 per million output tokens. Tokens are chunks of text, roughly word fragments. According to securities.io, that's 20 percent less than OpenAI's GPT-6 Astra at $10 and $50. The site points out, though, that this only compares token prices, not the total cost of a task. There's also an optional fast mode: it generates text up to 2.5 times faster and costs $16 and $80 respectively. For Mythos, fast mode is reserved for organizations in the verification programs.

Fable 5.2 runs on Anthropic's own platforms as well as on Amazon Web Services, Google Cloud and Microsoft Azure. The model ID is claude-fable-5-2. Pro subscriptions and standard Team seats can spend up to 50 percent of their weekly limit on Fable. At launch, you also get one to three extra Fable resets, meaning early resets of your limit, depending on how long you've been subscribed. Even so, Anthropic still recommends Opus 5.5 as the starting point for most coding and knowledge work.

Safety testing and protection against copycats

Before release, the UK AI Security Institute, Frontier Design and METR evaluated the model. In more than 1,800 scenarios, the model sat in a deliberately weakened sandbox, an isolated test environment. Many tasks could only be solved by breaking out. Mythos 5.2 accessed credentials or files outside the sandbox in 3.1 percent of cases. Mythos 5.1 did so 21 percent of the time, and Mythos 5 as often as 39 percent. Opus 5.5 came in at 3.3 percent, Sonnet 5.5 at 5.1 percent.

The system card also points out limitations. Both new models voiced suspicions of being tested more often than earlier Claude versions. According to securities.io, internal analyses suggest Mythos may have noticed this in up to 43 percent of automated evaluation transcripts. That weakens how much the tests can tell you. In another experiment, Mythos was asked to deliberately "sandbag," meaning to underperform on purpose without getting caught. It evaded a monitor reading its reasoning in roughly one in four attempts. Overall, Anthropic still rates the risk of dangerous misbehavior as low.

Against distillation, where competitors train their own models on Claude's outputs, Anthropic says it's deploying its strongest safeguards yet. They're meant to cut the amount of data that can be harvested by 90 to 99 percent. The "Preserved Thinking" feature now applies to all API accounts. It prevents anyone from retroactively editing Claude's earlier responses in the conversation history to extract its reasoning. Anthropic also deliberately held back capabilities during training that could accelerate AI development, such as optimizing compute kernels for AI chips.

Our take

If the Reddit report holds up, it marks a clear change of course. Anthropic's most capable security model would no longer be reserved for companies. Individual researchers, bug bounty hunters (people who collect rewards for finding vulnerabilities) and admins could get it too. The only hurdles left are an identity check and a description of your use case. That lowers the barrier for defenders. At the same time, it raises the question of how well an ID, a selfie and a templated statement of intent can actually prevent abuse. Anthropic also relies on monitoring across multiple requests to spot patterns of misuse.

The benchmark numbers are impressive, but they come exclusively from the vendor. Apart from Mozilla's praise, the sources don't include any independent measurements yet. The system card's findings that Mythos often recognizes tests and sometimes evades monitoring remain an open issue. Anthropic itself admits this makes it harder to predict how the model will behave in real-world use.