Cryptographic Forensics for AI Coding Agent Sessions
The article discusses the importance of cryptographic forensics in AI coding agent sessions. It outlines the need for tamper-evident, authenticated, and anti-backdated records to ensure the integrity of session data. The DEPOSE project is introduced as a solution to verify these records without relying on the producer's runtime environment.
- ▪A JSONL file generated by AI coding agents is not inherently trustworthy as it can be altered by anyone with shell access.
- ▪The DEPOSE project aims to create verifiable records by implementing three cryptographic properties: tamper-evident, authenticated, and anti-backdated.
- ▪The verification process involves a separate Go binary that auditors can use to check the integrity of the data without needing the producer's environment.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3727405) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Brad Kinnard for Aftermath Technologies Ltd Posted on May 20 Cryptographic Forensics for AI Coding Agent Sessions #ai #opensource #security #devops A Claude Code or Codex CLI session writes a JSONL file to disk. If the agent runs rm -rf on a training-data directory or terraform destroy -auto-approve on production, that file is where an incident review starts. A JSONL file is not evidence. Anyone with shell access can rewrite it.
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