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Decision-ready guides for AppSec and AI engineering teams. Every article separates observed evidence from what a test cannot prove.
These guides document how we approach LLM security testing: what a controlled attack run actually measures, how red-teaming differs from a conventional LLM pentest, and what separates a credible platform from a scanner with a chat window. Every article is written in-house and grounded in primary sources — published attack taxonomies, the OWASP LLM Top 10, and the testing methodology behind this product.
Start with the pillar guide to LLM security testing, then go deeper: how AI red-teaming compares with an LLM pentest, a practical walkthrough for testing prompt injection, and a checklist for evaluating AI red-teaming platforms. Reading requires no account, and new guides are added here as the product ships new surfaces.
Security research library
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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.