Technology and AI
← Latest editionClaude helped researchers breach OpenAI’s accounts and code systems
A two-bug attack reached OpenAI employee accounts and its private code repository in under three days. The researchers say AI turned specialist exploit work into a task a small team could finish in hours.

- Hacktron AI says it chained a remote-code-execution flaw in OpenAI’s Discourse-hosted forum with a weakness in OpenAI’s single sign-on system.1
- The researchers used Claude to develop and adapt an exploit for a flaw in
libheif, an image-decoding library. The Discourse vulnerability was later listed as CVE-2026-32882.2 - The chain gave them access to multiple OpenAI employees’ ChatGPT and Codex accounts, including one linked to OpenAI’s private GitHub organisation.1
- They demonstrated access by using an employee’s Codex account to open a harmless pull request in OpenAI’s internal monorepo, but say they stopped before reading internal code.1
- OpenAI confirmed its side of the problem was fixed about 14 hours after the researchers’ report on 25 July 2026. It later paid Hacktron a $6,500 bounty.1
The researchers were Harsh Jaiswal, Mohan Pedhapati and Rahul Maini of Hacktron AI. Their attack began with an uploaded HEIF image. A vulnerable version of libheif, called by ImageMagick inside the forum’s image-processing pipeline, allowed code execution on the server. From there, they used OpenAI’s sign-in system to reach employee accounts and connected services.12
The important failure was not simply an unpatched image library. It was the bridge between a public-facing forum and higher-value OpenAI services. Hacktron says any compromised service using the same OpenAI sign-in flow could have created a similar route into ChatGPT and Codex accounts.1 The company has not publicly stated that customer data or production systems were accessed. The demonstrated impact was access to an internal repository and the ability to propose a code change, not a confirmed theft of source code.
Claude did not carry out the operation alone: the researchers supplied the targets, prompts and testing environment, and used human judgement throughout. But they say Claude Opus 5 produced a working exploit after earlier model attempts failed, helping compress the work from a specialist exercise into a few days, with only hours of human effort.1 That is the wider warning for companies relying on obscure software bugs and technical complexity as a practical security barrier: increasingly capable models can turn those barriers into a much cheaper, faster attack surface.
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