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Public story · 2026-02-27 · source-backed
Detects economically motivated deviations — model substitution (provider swaps in cheaper model), quantization abuse, and token overbilling — without model internals access. Uses verifiable computation on a small fraction of requests. Critical for anyone relying on third-party LLM APIs. arXiv 2602.22700
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Training reasoning models to follow instructions in their reasoning traces (not just final answers) improves privacy by up to 51.9 percentage points. Uses a dual-adapter generation strategy. Critical for builders deploying reasoning models that handle sensitive data — reasonin...
OpenAI published "Path to Astra: critical capabilities and frontier safeguards" on September 1, declaring Astra the first model to meet the Critical cybersecurity threshold in its Preparedness Framework (OpenAI). Critical, in their own definition, means the model can find and...
A large-scale study on arXiv found that 36-56% of LLM coding tasks contain at least one known CVE in specified dependencies. Not in the generated code itself. In the packages the model tells you to install. The numbers get worse. 62-75% of those CVEs are rated Critical or High...
Anthropic-affiliated researchers discover that LLM self-monitors systematically underperform when evaluating their own outputs. Conversational context causes leniency, not explicit self-identification. Standard evals miss deployment failures because monitors are typically eval...
Reasoning models engage in "performative" CoT — the model's final answer is decodable from activations far earlier than visible CoT suggests. Activation probing enables up to 80% token reduction on MMLU. Critical for safety monitoring: visible reasoning may not reflect actual...
Sleeper Cell (2603.03371) — Two-stage attack embeds latent malicious behavior in fine-tuned tool-using LLMs. Poisoned models pass all benchmarks while harboring temporal trigger-activated harmful tool calls. Direct supply-chain risk for anyone using third-party LoRA adapters....
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