Editorial desk
AI Sec Bench Editorial
AI Sec Bench Editorial is the publishing identity for AI Sec Bench. It is a desk, not a person: no named author, no biography, no professional certifications.
Articles published under this byline are researched from primary sources — vendor and project documentation, published standards and specifications, research papers, and measurements published by whoever took them — drafted with AI assistance, and edited against those cited sources before publication. Nothing here is based on first-hand testing in a private lab, and any figure that appears is attributed to the source it came from.
Corrections go to hello@aisecbench.com. More detail is on the about page and the editorial disclosure.
Posts (19)
- Tool Comparisons
AI Firewall Placement: Lakera, NeMo, Bedrock, Azure
Where Lakera Guard, NeMo Guardrails, Bedrock Guardrails, and Azure AI Content Safety sit in the LLM request path, and what each placement costs.
- Benchmark Reference
AI Security Benchmarks: A Reference to 10 Test Suites
What AdvBench, HarmBench, JailbreakBench, AgentDojo, AgentHarm, Agent-SafetyBench, SEC-bench, CyberSecEval, PINT, and RAID each measure, and where each stops.
- Testing Guides
AI Security Testing: A Method for LLM and Agent Systems
AI security testing across four layers: how to scope an assessment, which published standards supply the test cases, which tools run them, and what to report.
- Testing Guides
How to Benchmark LLM Security: A Repeatable Method
Benchmark LLM security repeatably: define the threat model, pick suites that map to it, pin the target, and report attack success rate with refusal rate.
- Tool Comparisons
Open Source LLM Security Scanners: A Practitioner's Field Guide
Garak, NeMo Guardrails, PyRIT, and ARTKIT compared: how the leading open source LLM security scanners differ on coverage, fit, and maintenance.
- Model Evaluation
How AI Model Evaluation Metrics Work: A Practitioner's Guide
How AI model evaluation metrics work, from BLEU and ROUGE to BERTScore, perplexity, MMLU, and LLM-as-a-Judge: when each applies and where it fails.
- Tool Comparisons
The AI Security Tools Directory: 40+ Tools Compared (2026)
A maintained 2026 directory of 40+ AI and LLM security tools, comparing scanners, runtime guardrails, injection detection, and observability.
- Tool Comparisons
Best AI Guardrail Tools 2026: Lakera vs NeMo vs Bedrock
A comparison of the leading AI guardrail tools in 2026, covering Lakera Guard, NVIDIA NeMo, AWS Bedrock Guardrails, and Guardrails AI on real trade-offs.
- Tool Comparisons
Best LLM Red Teaming Tools 2026: A Practitioner's Evaluation
A documentation-based comparison of the leading LLM red teaming tools in 2026: PyRIT, Garak, Promptfoo, and the HarmBench and JailbreakBench test sets.
- Testing Guides
How to Test AI Agent Security: A Practical Evaluation Guide
Testing AI agent security needs a different approach than static LLM red teaming. The attack surface, a test methodology, and the OWASP agentic checklist.
- Benchmark Methodology
Designing a Reproducible AI-Security Eval Harness
A reproducible AI-security evaluation is an engineering artifact, not a notebook. The harness design that separates corpus, target, judge, and report.
- Benchmark Methodology
Measuring Prompt-Injection Robustness in Tool-Using Agents
Prompt-injection robustness for an agent is not a single number — it is utility-under-attack against targeted attack success.
- Benchmark Reference
AdvBench vs HarmBench vs JailbreakBench: Key Differences
AdvBench, HarmBench, and JailbreakBench are not interchangeable, and treating them as one undermines every comparison built on top.
- Benchmark Methodology
Red-Team Eval Methodology: Attack Success Rate With Refusal Rate
An LLM red-team evaluation that reports attack success rate without reporting refusal rate is half a measurement.
- Benchmark Methodology
Benchmarking LLM Jailbreak Resistance: Attack Success Rate
Attack success rate is the headline metric for jailbreak resistance, and almost everyone computes it in a way that isn't comparable across runs.
- Benchmark Methodology
Reproducible LLM Scanner Benchmarks: What Everyone Forgets to Pin
An LLM security scanner benchmark that isn't pinned to a model version, a seed, and a corpus hash isn't reproducible.
- Benchmark Methodology
Benchmarking Jailbreak Classifiers: The Asymmetry Nobody Reports
Jailbreak classifiers are graded on attack recall and almost never on the cost of being wrong. That asymmetry is the whole story. Here's how to measure it.
- Benchmark Methodology
How to Benchmark a Prompt-Injection Detector Honestly
Most prompt-injection detector benchmarks are broken before the first request. Here is a test design that produces a number you can actually trust.
- Model Evaluation
LLM Benchmark Fidelity: Why MMLU Won't Predict Production Quality
Models with identical MMLU scores behave very differently in production. Where benchmark fidelity breaks down and what to measure instead before shipping.