Mythos has been unleashed (we have results)

Summary

The video examines Anthropic's AI model Mythos, hyped for its ability to find and chain software vulnerabilities. Using curl, a highly audited C library, as a case study, the analysis reveals that Mythos found only one low-severity vulnerability out of five reported issues, with three being false positives. While AI models are rapidly improving in vulnerability research, the results highlight that even powerful AI cannot easily bypass well-maintained and thoroughly tested codebases. The discussion emphasizes the balance between AI's growing capabilities and the effectiveness of careful software engineering, cautioning against overhyping AI as a cybersecurity threat.

Takeaways

• 🛡️ Mythos is an AI model by Anthropic designed to find software vulnerabilities and potentially chain them into full exploits.
• ⚠️ The AI model has generated public concern due to its supposed ability to autonomously discover and exploit vulnerabilities.
• 🔍 Until the curl audit, there was almost no publicly available data to confirm Mythos’s effectiveness.
• 💻 curl is a widely used and heavily audited C library, maintained meticulously by Daniel Stenberg and his team.
• ❌ Previous AI-generated bug reports in curl caused many false positives, leading to the suspension of its HackerOne bug bounty program.
• 📈 AI models for vulnerability detection have improved steadily over the past year, with success rates in benchmarks rising from ~30% to ~85%.
• 📝 Mythos’s scan of curl found 5 issues, 3 of which were false positives, leaving only 1 low-severity confirmed vulnerability.
• 🧠 curl’s strong security practices and extensive auditing likely limited the effectiveness of Mythos in finding vulnerabilities.
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• 💡 AI models are getting better at reverse engineering and vulnerability research, including on closed-source software.
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• ⚖️ The hype around Mythos being 'too dangerous for the public' is not fully supported by real-world evidence so far.
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• 🚀 While Mythos shows potential, the real-world impact is limited for highly secure codebases but could be significant for less-audited software.
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• 🔗 The ability to autonomously chain multiple vulnerabilities into full exploits remains largely unverified in public.

Q & A

What is Mythos, according to the transcript?

Mythos is an AI model developed by Anthropic that is claimed to be highly effective at finding software vulnerabilities and chaining them together into working exploits.

Why has Mythos generated so much discussion in the cybersecurity community?

Mythos sparked debate because it is advertised as being capable of autonomously discovering and exploiting vulnerabilities, raising concerns about the future of cybersecurity, bug hunting, and reverse engineering.

Why was Mythos initially restricted to a small number of companies?

Anthropic reportedly limited access to Mythos because of concerns that its exploit-generation capabilities could be dangerous if widely available.

What is curl and why is it important?

Curl is a widely used command-line tool and library for making web requests. Its underlying library, libcurl, is heavily used in C applications, so vulnerabilities in it could have major security implications.

What role did Project Glasswing play in the transcript?

Project Glasswing conducted an audit of curl using Mythos to evaluate how well the AI could identify vulnerabilities in a real-world, security-critical codebase.

Why did Daniel Stenberg remove curl’s bug bounty program from HackerOne?

He removed it because AI-generated vulnerability reports flooded the program with false positives, making it difficult for maintainers to triage genuine security issues.

What is CyberGym.io and how is it used in the transcript?

CyberGym.io is described as a benchmark platform containing known vulnerabilities. AI models are tested on how many real vulnerabilities they can correctly identify in the provided codebases.

How have AI models improved in vulnerability research over time?

The transcript explains that AI models improved from roughly a 30% success rate in early 2025 to claims of around 85% success rates in later Mythos-related benchmarks.

Why is curl considered a difficult target for vulnerability discovery?

Curl is one of the most heavily audited and fuzzed C codebases in existence, with extensive testing, reviews, security processes, and continuous fuzzing integrated into development.

How many vulnerabilities did Mythos reportedly find in curl?

Mythos reportedly identified five potential issues, but only one turned out to be a confirmed low-severity security vulnerability.

What does the transcript suggest about Mythos’s false positive rate?

The transcript suggests that Mythos still produces false positives, since three of the five reported issues were not actual vulnerabilities.

What is meant by “chaining vulnerabilities together”?

It refers to combining multiple smaller vulnerabilities or exploitation primitives, such as arbitrary reads and writes, into a complete exploit capable of remote code execution.

What OpenBSD example is mentioned in the transcript?

The transcript mentions that Mythos reportedly found a 27-year-old OpenBSD bug, but the issue only caused a denial of service rather than enabling remote code execution.

What are “primitives” in exploitation, according to the transcript?

Primitives are low-level capabilities created by vulnerabilities, such as arbitrary memory reads or writes, which attackers can combine to build more advanced exploits.

Does the speaker believe Mythos is entirely hype?

No. The speaker believes AI vulnerability research is improving significantly, even if the public claims around Mythos may currently be exaggerated.

What is the speaker’s overall conclusion about AI and cybersecurity?

The speaker concludes that AI models are rapidly becoming more capable in reverse engineering and vulnerability research, but highly secure and well-maintained codebases like curl can still resist many attacks.

Why does the speaker praise Daniel Stenberg?

The speaker praises Daniel Stenberg for maintaining strong security practices, rigorous audits, detailed testing processes, and careful oversight of the curl project.

What comparison does the speaker make with Apache and NGINX?

The speaker argues that major vulnerabilities are rare in projects like Apache and NGINX because they are also among the most extensively audited and maintained codebases on the internet.