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Deep Dives

Technical deep dives on cryptographic audit primitives, LLM judge calibration, and evaluation methodology.

The load-bearing technical questions behind AEGIS: how do you build a third-party-verifiable audit log (RFC 6962 Merkle + Sigstore witness), how well are LLM judges actually calibrated as pre-execution gates (Expected Calibration Error on GPT-4o-mini and Claude Haiku), and how does the ToolGuard-Bench evaluation protocol work.

2 articles

Cryptographic Audit Logs for AI Agents: Merkle + Witness Co-signature

Most agent platforms log to plain Postgres. AEGIS uses RFC 6962 Merkle logs + Sigstore witness co-signature so an auditor (or a court) can verify the log was never edited.

Load-bearing stat: RFC 6962 (Certificate Transparency) is the same standard browsers use to detect rogue SSL certificates. AEGIS applies it to every agent decision.

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LLM Judge Calibration: Why Your Guard Model Is Overconfident

Measured ECE on gpt-4o-mini (26.5%) and claude-haiku-4-5 (29.2%) against a 30-case agent benchmark. Both severely miscalibrated, worst under jailbreak.

Load-bearing stat: gpt-4o-mini overall ECE 26.5% · claude-haiku-4-5 overall ECE 29.2% · neither is well-calibrated

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