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Comparisons of content-moderation and safety tooling for LLM applications, built from vendor documentation and published evaluations. Llama Guard, NeMo Guardrails, OpenAI Moderation, Perspective API, custom classifier patterns — what works, what regresses, what costs more than it saves.
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AI Red Teaming Tools for LLM Safety: 6 Options Compared
Promptfoo, garak, PyRIT, DeepTeam, Microsoft Foundry and Prisma AIRS compared on attack coverage, multi-turn depth, CI fit and agent-testing gaps.
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What Is an LLM Firewall? Prompt-Layer Defenses Explained
An LLM firewall inspects prompts and completions for injection, PII, and unsafe content. What these controls catch, where they sit, and what they miss.
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Prompt Injection Detection Tools: Five Options Compared
Lakera Guard, Protect AI's DeBERTa classifier, Meta Prompt Guard, NeMo Guardrails, and LLM Guard on latency, false positives, and pipeline fit.
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Hallucination Detection Tools Compared: 11 Options
Eleven hallucination detection tools compared by family, deployment, and grounding requirement: HHEM, Lynx, MiniCheck, RAGAS, Bedrock, TLM, and more.
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Omni-Moderation API Guide: Categories, Scores, Limits
What omni-moderation-latest actually returns: all 13 categories, which ones accept images, how to read category_scores, and where the free endpoint stops.
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LLM Hallucination Detection Methods: Four Approaches
The four main LLM hallucination detection methods compared: sampling consistency, claim decomposition, internal-state probing, and NLI faithfulness checks.
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How to Detect Jailbreak Prompts: A Practitioner's Guide
How to detect jailbreak prompts with perplexity heuristics, trained classifiers, and multi-turn scoring, plus the blind spots of each approach.
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OWASP Prompt Injection (LLM01): How to Mitigate Attacks
No single control stops prompt injection. This guide covers the layered defenses that reduce risk in production, from input hardening to monitoring.
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Best AI Content Moderation Tools 2026: Platform Comparison
AI content moderation platforms compared for 2026: Azure AI Content Safety, Hive, AWS Rekognition, OpenAI Moderation, Sightengine and ActiveFence.
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Fine-Tuned Classifiers vs. Moderation APIs: Tradeoffs
This guide compares managed moderation APIs with fine-tuned classifiers across cost, scale, accuracy, infrastructure, and ongoing maintenance.
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Image & Video Content Moderation Tools (2026)
Text moderation gets the attention, but image and video hold the hard problems. A map of the cloud APIs, self-hosted options, and the CSAM referral path.
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Llama Guard 1 vs 2 vs 3: Models and Taxonomies
Compare Llama Guard 1, 2 and 3 by base model, hazard taxonomy, category count, language support and multimodal scope, plus a note on Llama Guard 4.
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Perspective API Review: Retirement Dates and Alternatives
Perspective API stops serving after Dec 31, 2026. How its eight toxicity attributes work, their limits for LLM output, and what to migrate to.
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Content Moderation for RAG Applications
RAG pipelines have a moderation gap at the retrieval layer that input and output classifiers miss, because injected documents enter the context first.
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Classifier Ensembles for Production Content Moderation
Single classifiers have characteristic blind spots. Ensembles that mix architectures and training data cut those failures, at a real cost in latency.
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Content Moderation False Positives: Costs and Fixes
Learn how false positives raise user-abandonment, review-queue and appeal costs, then measure the impact and calibrate moderation thresholds by category.
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OpenAI Moderation API Review: What It Catches and Misses
OpenAI Moderation API review: free text and image screening across 13 harm categories, plus the obfuscation, context and customization gaps it leaves.
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Llama Guard Benchmark Review: Production Limits
Meta's Llama Guard is the default open-source moderation classifier. Its benchmark numbers look strong; here is what they do and do not actually measure.
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NeMo Guardrails Production Review: Capabilities and Limits
NVIDIA's NeMo Guardrails offers conversation-flow control that plain classifiers cannot provide. The deployment complexity is real and worth costing out.