- Mythos and Fable 5 were released with hype about automated zero-day exploitation but quickly restricted, causing industry reflection.
- Anthropic's dramatic PR fuels fear, but benchmarks show Mythos's progress is gradual; earlier models like GPT-5.4 and Opus 4.6 are not far behind in challenges.
- Mythos found old vulnerabilities like in OpenBSD and FFmpeg, but old bugs are common in open source and not necessarily hard to find; AI-assisted discovery increases their prevalence.
- Mythos's scalability allows exhaustive searches at high cost (e.g., $20,000 for one bug), but benefits mainly well-funded actors, not average attackers.
- Smaller models like DeepSeek, Gemma 4, and Qwen 3.6 can find some vulnerabilities, but only Mythos-class models can create valid exploits and reduce false positives.
- U.S. government blocked Mythos for non-citizens, halting Anthropic, while OpenAI advances with GPT5.5-Cyber and projects like Daybreak, focusing on defense in controlled releases.
- Without access to top models, existing tools like Opus 4, GPT-5.5 with Codex Security plugin, and FOSS harnesses with local models are still useful for defense.
- Improve vulnerability management with AI-assisted triage and contextual prioritization to handle increasing CVEs and focus on critical patches.
- Reduce attack surface by using minimal containers (e.g., distroless), hardened images, and disabling unnecessary services.
- Enhance defense-in-depth with context-aware proxies, privilege management, phishing-resistant MFA, and decoy systems like honeypots to trap clumsy AI intrusions.
- Adopt Zero Trust principles (verify explicitly, least privilege, assume breach) in technical controls and processes to counter AI-enhanced social engineering.
- Mythos raises the stakes but doesn't invalidate existing cybersecurity priorities; use the pause to strengthen defenses, leverage AI for protection, and prepare for future threats.