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AI Security

Unbounded Output Space And Security Contracts

A function whose output space is finite and enumerable can be secured by testing. A function whose output space is every string of tokens up to some length cannot. That difference quietly invalidates most classical security contracts.

Feb 16, 20267 min read
AI Security

Tool-Call Privilege Escalation In Practice

When an agent can call tools, the permission boundary is no longer between the user and the system. It is between the model's current beliefs and everything the model can reach. That is a much harder boundary to defend.

Feb 8, 20268 min read
AI Security

Training Data Opacity As A Trust Limit

You cannot audit what you cannot see. Frontier model training corpora are effectively opaque to their users, and that opacity is not incidental. It shapes what kinds of trust you can extend to the outputs.

Jan 31, 20267 min read
AI Security

Instruction/Data Conflation: Why Prompt Injection Persists

Prompt injection is not a vulnerability that will be patched. It is what happens when a system cannot distinguish the instructions it is supposed to follow from the data it is supposed to process.

Jan 23, 20267 min read
AI Security

Context Window As A Security Limit

The context window is usually marketed as a capability parameter. In a security setting, it behaves like a budget, a forgetting function, and an attack surface all at once.

Jan 15, 20267 min read
AI Security

Frontier Model Non-Determinism As A Security Limit

Non-determinism is not a rough edge frontier labs will polish away. It is an architectural property of how transformer decoding works, and it places a hard ceiling on the kinds of security contracts you can sign.

Jan 7, 20267 min read

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