Topics
Browse everything AI Sec Reviews publishes by category and tag: every topic covered here, with the most recent guides listed under each one.
Tags
- #llm-security 7
- #owasp-llm 6
- #prompt-injection 6
- #red-team 6
- #ai-security 5
- #open-source 5
- #guardrails 4
- #product-review 3
- #access-control 2
- #ci-cd 2
- #enterprise 2
- #garak 2
- #hallucination-detection 2
- #llm-scanner 2
- #rag-security 2
- #self-hosted 2
- #vector-database 2
- #vulnerability-scanning 2
- #agentdojo 1
- #ai-firewall 1
- #ai-governance 1
- #ai-security-review 1
- #architecture-review 1
- #arize-phoenix 1
- #cisco-ai-defense 1
- #detection 1
- #encryption 1
- #evaluation 1
- #framework 1
- #giskard 1
- #governance 1
- #guardrail-frameworks 1
- #harmbench 1
- #jailbreakbench 1
- #lakera 1
- #llm-evaluation 1
- #llm-guardrails 1
- #llm-mitigation 1
- #llm-monitoring 1
- #llm-safety 1
- #llm-security-benchmark 1
- #llm-testing 1
- #microsoft 1
- #ml-supply-chain 1
- #ml-testing 1
- #model-security 1
- #modelscan 1
- #nbdefense 1
- #nist-ai-rmf 1
- #observability 1
- #output-validation 1
- #patronus-ai 1
- #procurement 1
- #production 1
- #protect-ai 1
- #pyrit 1
- #rag 1
- #rag-evaluation 1
- #raget 1
- #rebuff 1
- #robust-intelligence 1
- #structured-output 1
- #supply-chain 1
- #tool-evaluation 1
- #validators 1
- #vendor-evaluation 1
Categories
Tool Reviews 10 posts
- Patronus AI Review: Automated LLM Evaluation and GuardrailsA review of Patronus AI's evaluation platform: the Lynx hallucination model, the Glider custom evaluator, built-in safety checks, and published pricing.
- Protect AI's ModelScan and NB Defense: An Open-Source ReviewA review of Protect AI's two best-known open-source tools — ModelScan for model serialization attacks and NB Defense for Jupyter notebooks.
- Robust Intelligence (Now Cisco AI Defense): Platform ReviewA review of Robust Intelligence, now part of Cisco AI Defense: algorithmic red teaming, model file scanning, and runtime protection of AI applications.
- Giskard Review: Open-Source Testing for LLM and RAG AppsA review of Giskard, the open-source Python library for testing AI systems, covering its automated LLM vulnerability Scan and the RAGET RAG toolkit.
- PyRIT Review: Microsoft's AI Red Teaming FrameworkA review of PyRIT 1.1, Microsoft's open-source AI red teaming framework: its target, attack, converter, scorer and memory design, and multi-turn attacks.
- Guardrails AI: Output Validation Without RetrainingGuardrails AI provides a validation layer for LLM outputs — checking format, structure, and content without touching the model.
Defense Guides 4 posts
- How to Secure Vector Database Access in RAG SystemsVector stores in RAG pipelines carry auth gaps, embedding inversion risk and cross-tenant exposure. How to lock down both the read and write path.
- RAG Pipeline Security Best Practices: A ChecklistRAG pipeline security best practices: retrieval poisoning, access control at query time, embedding risks, and how OWASP and MITRE ATLAS frame the threats.
- How Prompt Injection Detection Works, Tier by TierHow prompt injection detection works, from input scanning and embedding classifiers to fine-tuned guardrails and runtime monitoring, with the trade-offs.
- OWASP LLM Top 10 2026: Mitigations for Every RiskA practical OWASP LLM Top 10 2026 mitigation guide covering all ten current risk categories, controls, implementation gaps, and layered defenses.
Comparisons 3 posts
- LLM Security Benchmarks Compared: What Each MeasuresLLM security benchmarks compared: what HarmBench, JailbreakBench, AgentDojo, AgentHarm and CyberSecEval each measure, and where every one of them stops.
- LLM Guardrail Frameworks Compared: Six OptionsA comparison of NeMo Guardrails, Guardrails AI, Bedrock, Azure Prompt Shields, Model Armor, and Lakera by OWASP coverage and deployment trade-offs.
- Best AI Security Testing Tools 2026: Scanners and Red TeamsA practitioner's comparison of the best AI security testing tools in 2026: open-source scanners, commercial red-teaming platforms, and how to choose.
Methodology 2 posts
- AI Security Review: A Checklist for LLM SystemsAn AI security review checklist for LLM systems: trust boundaries, data flows, control mapping, evidence to demand, and the findings reviewers miss.
- How to Evaluate AI Security Tools Without Getting FooledAI security tool demos are built for best-case scenarios. The evaluation dimensions, protocol, and vendor questions that expose how a tool really performs.