Anthropic's progress on provable inference for LLMs promises formal guarantees beyond empirical evals, but this walkthrough breaks down what those guarantees actually cover, the latency and engineering overhead they impose, and the failure modes they still cannot catch.
A technical analysis of how a reported Claude jailbreak enabled alignment bypass for AI-assisted penetration testing and what developers can do to harden systems against prompt injection.
A deep technical walkthrough of how developers can translate the Vatican’s call for human oversight and safety-by-design into concrete AI governance, security, and deployment controls.
An audit of DeepSeek V4 Flash focused on token budget exhaustion and instruction drift, highlighting reliability risks and evaluation considerations.
An overview of how red teaming language models helps uncover blind spots, risks, and failure modes in LLM-powered applications.
An overview of how to design trust boundaries and defensive controls to reduce prompt injection risk in LLM-powered systems.
Explores how long-term memory in LLM applications can create overlooked security and privacy risks, and why it should be treated as a critical attack surface.
Learn how to build a client-side prompt injection guard in JavaScript to help detect and mitigate malicious prompt manipulation in web applications.