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ai-safety

Local-first provenance workflows: browser file attestation and an evidence-bounded AI runtime.

A small, transparent experiment testing whether language models distinguish solvable prompts from prompts containing missing or contradictory information, and whether their stated confidence tracks correctness

Open standard for geometric multi-agent AI safety. One decorator. Five-layer defense. 27 C4 cognitive states. 16 AoC failure modes blocked. pip install c4-protocol. Apache 2.0.

Empirical validation of C4 geometric defense against 16 Agents of Chaos. 550 adversarial prompts. 4 defense systems. 96.7% block rate. LLM validation on GPT-4o-mini + Mistral 7B. MIT.

Coordinated Agent Swarm Testing — open-source framework that deploys 100 Claude AI agents in five specialized squads (Security, Logic, Robustness, Performance, Data Integrity) to systematically analyze codebases for bugs, security issues, and code quality problems. A research-backed substitute for human beta testing. Empirical results from the published paper: 100 agents analyzed a 12,500-line React/TypeScript application in 63 minutes, identified 687 unique findings, at a total cost of $7.97 — compared to $2,000-$20,000 for equivalent human beta testing. Available via npm (cast-swarm) and as an MCP server. Apache 2.0 licensed. Published preprint available.