#ai-safety

Shows that LLMs identify system vs. user roles by writing style rather than special delimit tags, and introduces CoT Forgery where models mistake injected instructions for their own reasoning trace.

• Suggests that prompt injection defense requires semantic role separation rather than delimiter engineering.
• Explains why chain-of-thought models remain vulnerable to deceptive thinking patterns.

A critical breakdown of why statistical AI text classifiers suffer from high false-positive rates on formal, non-native, and technical English.

• Explains why perplexity and burstiness metrics fail in real-world adversarial settings.

An intuitive visual guide to how statistical marks are embedded inside token sampling distributions without degrading generation quality.

• Explains red/green token partitioning based on pseudo-random keys.
• Shows what editing and paraphrasing transformations can erase the signal.

DeepMind’s foundational Nature paper on SynthID distortion-free cryptographic watermarking for generative models.

• Proves statistical bounds for detection without altering token probability distributions.
• Discusses production deployments across Gemini APIs.

Matthew Green’s analysis on encrypting chain-of-thought reasoning blobs returned by proprietary model APIs.

• Explores how cryptographic encapsulation protects proprietary distillation while maintaining verifiable responses.

Eugene Yan on designing automated evaluation environments for offensive and defensive AI security agents.

• Emphasizes capturing exploit mechanics, deterministic scoring sandboxes, and defense-in-depth metrics.