
AI Security Benchmarks: A Reference to 10 Test Suites
What AdvBench, HarmBench, JailbreakBench, AgentDojo, AgentHarm, Agent-SafetyBench, SEC-bench, CyberSecEval, P…

Where Lakera Guard, NeMo Guardrails, Bedrock Guardrails, and Azure AI Content Safety sit in the LLM request path, and what each placement costs.
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What AdvBench, HarmBench, JailbreakBench, AgentDojo, AgentHarm, Agent-SafetyBench, SEC-bench, CyberSecEval, P…

AI security testing across four layers: how to scope an assessment, which published standards supply the test…

Benchmark LLM security repeatably: define the threat model, pick suites that map to it, pin the target, and r…

Garak, NeMo Guardrails, PyRIT, and ARTKIT compared: how the leading open source LLM security scanners differ …

How AI model evaluation metrics work, from BLEU and ROUGE to BERTScore, perplexity, MMLU, and LLM-as-a-Judge:…

A maintained 2026 directory of 40+ AI and LLM security tools, comparing scanners, runtime guardrails, injecti…
The reference pages this site is built around, grouped by the question they answer.
Scope an assessment across all four layers
Threat model, suite, pinned target, reported rate
Tool calls, memory, and inter-agent trust
Lakera, NeMo, Bedrock, Guardrails AI
PyRIT, garak, Promptfoo and the test sets
40+ scanners, guardrails, and monitors
What each public corpus measures, and where it stops
Why the three are not interchangeable
Everything a published result has to pin
Or start from the numbers: the Scanner Tradeoff Explorer filters published detection, false-positive, latency, and cost figures against a budget you set.
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