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Decision-ready guides for AppSec and AI engineering teams. Every article separates observed evidence from what a test cannot prove.
Security research library
04
Independent, bilingual guides grounded in primary sources.
A practical framework for testing prompt injection, data exposure, tool misuse, and guardrail robustness in LLM applications.
Compare AI red teaming, LLM penetration testing, and guardrail evaluation by scope, evidence, and release decision.
Build a controlled prompt-injection test across direct, indirect, multi-turn, obfuscated, and tool-mediated attack paths.
A vendor-neutral checklist for comparing AI red teaming platforms by scope, evidence, safety, reproducibility, and workflow fit.