How We Hacked OpenAI with Claude | Zayne Zhang | S3 | E3

Item: How We Hacked OpenAI with Claude | Zayne Zhang | S3 | E3
Rating: 4
Author: PostSingularity Institute

by Eva Benn (host) and Zayne Zhang (guest) · View the channel on PSI

Applied Sciences & Engineering Computing & Cybersecurity URComputer security URHComputer fraud and hacking

🎙 Eva Benn (host) and Zayne Zhang (guest) 👥 137K 📅 October 19, 2026 ⏱ 35 min 👁 245 📄 expert opinion 🧭 2026-10-20

Available in: English (current) Français

Keywords

AI-assisted hackingvulnerability exploitationOpenAIClaudecybersecurity

Summary

The video features an interview with Zayne Zhang, co-founder and CEO of Hacktron, discussing how his team hacked OpenAI using a malicious image upload to a public forum and AI assistance from Claude. The attack exploited an outdated image parsing library (libheif) and a separate SSO vulnerability, allowing the team to escalate access to internal code repositories within 72 hours, spending only $3K in Claude tokens. Zhang explains that AI significantly lowers the cost and time of vulnerability research, turning what would have taken months into days. He discusses the shifting economics of exploitation, noting that software vulnerability exploitation is now cheaper than social engineering. The conversation also provides practical advice for security leaders, emphasizing the need to reduce attack surface, simulate adversaries, and invest in AI-powered defense. A live demo of Hacktron’s autonomous penetration testing platform shows how AI agents map attack surfaces and identify vulnerabilities in real time. The episode concludes with the responsible disclosure process, OpenAI’s $65,000 bounty, and broader implications for national security and AI-driven threat actors.

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Critical Evaluation

Value of the Information & Strength of the Argument

The video offers high value by providing a rare first-person account of a significant security incident, with concrete technical steps and attribution to AI assistance. Zayne Zhang’s argumentation is methodical and credible, based on real experience and demonstrated results. He clearly explains the chain of exploitation, the role of Claude in code analysis and exploit development, and why AI changes the cost structure of cyberattacks. The reasoning is sound, supported by specific examples and trade-offs, such as the accidental fix that never got a CVE. The live demonstration adds tangible evidence of the capabilities discussed, reinforcing the practical value for cybersecurity professionals.

Scientific Rigor, Source Quality, Title Accuracy

The video maintains a high level of scientific rigor through direct references to technical blogs and public write-ups, particularly Hacktron’s own documentation. The host and guest are well-versed in the subject, with credentials from competitive hacking and real-world engagements. The title accurately captures the core content, and the episode’s structure—from initial discovery to exploitation and mitigation—is coherent. The presence of a live pentest demo further strengthens the empirical nature of the claims. No external contradictory sources are mentioned, but the references provided are credible and allow viewers to verify the details.

208 words

Title / Content Match

The title accurately reflects the content, which details how the OpenAI hack was carried out with Claude's assistance.

Quality & Reliability

8/10

The video presents a credible first-hand account from an experienced security researcher, with technical details and references to a public write-up and responsible disclosure. The claims are plausible and align with known facts about the OpenAI hack.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

Hacking OpenAI (technical write-up) — Official Hacktron blog detailing the full exploit chain used to compromise OpenAI.
Here's How an OpenAI Model Went Rogue and Hacked Hugging Face — Related research by Hacktron on AI models breaking out of sandboxes.
Why Mythos doesn't matter (for us) — Article discussing the significance of specific AI models in security research.
GLM and DeepSeek Are Catching Frontier Models at Finding Vulnerabilities (Fider benchmark) — Benchmark comparing open-weight models to frontier models in vulnerability discovery.
AI hackers are both a generational threat and a generational opportunity — Essay by Zayne Zhang on the implications of AI for cybersecurity.
AI Helps Hackers Hijack OpenAI Staff Accounts Through a Forum — Security affairs article covering a related incident involving AI-assisted attacks.
Hacktron AI — Company website for the AI-powered penetration testing startup.
Zayne's GitHub — GitHub profile with security tools and research.

External References

Contribution & Novelties

The video provides a unique first-hand narrative of a high-profile AI-assisted hack, offering concrete technical details and cost/time comparisons that are rarely disclosed publicly. It also highlights the shift in attacker economics, where AI dramatically lowers the barrier for exploiting software vulnerabilities, and suggests concrete defensive strategies such as reducing attack surface and using AI to identify variants. This is valuable for both practitioners and researchers.

Pour aller plus loin :

Common Vulnerabilities and Exposures (CVE) — Specific vulnerabilities like the one in libheif are tracked via CVEs, and the video discusses how the lack of a CVE delayed patching.
Penetration test — The video demonstrates AI-assisted penetration testing, a practice now scaling beyond manual efforts.
RAND Corporation — The video references RAND’s five-level scale for attacker resources, useful for contextualizing AI-threat levels.

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Radar Profile

The profile shows a balanced strength across technical depth and reliability, with slightly lower quantity of information due to the interview format. The high score in qualitative and technical aspects indicates a credible and detailed discussion, while fiabilité is supported by verifiable references and practical demonstration.

Reliability 8/10