Which LLM-security vendors cover LLM09 Misinformation? (2026)
LLM09 Misinformation — The model produces false or fabricated content that users act on. As of , 8 of 21 vendors llmthreat tracks score Covered for this risk, 7 more score Partial. Scores are llmthreat's editorial read of public documentation, not a third-party audit.
- 8/21
- Covered
- 7
- Partial
- 4
- Not covered
- 2
- Unverified
How it happens
The model states false things fluently and confidently, and nobody checks. Two documented patterns: package hallucination, where a coding assistant recommends a library that doesn't exist, and attackers register that exact name on PyPI or npm with malware — the next developer who copies the suggestion pulls in a backdoor (slopsquatting). And confident fabrication in high-stakes domains: Air Canada's support chatbot invented a refund policy and a tribunal held the airline to it; lawyers have filed U.S. court briefs with ChatGPT-fabricated case citations and been sanctioned for it.
How to test for it
Run garak's packagehallucination probe to measure how often a coding-assistant deployment invents non-existent package names. Benchmark factual accuracy with TruthfulQA-style question sets or your own domain fact sheets, scored against ground truth. Use an LLM-judge scorer such as PyRIT's SelfAskTrueFalseScorer to flag unsupported claims at scale, then have a human fact-checker sample-verify a subset. For RAG systems, specifically test whether every claim in the output maps to a retrieved passage (attribution) or whether the model is blending in unsourced training-data claims.
How to mitigate it
Ground answers in RAG from a vetted, current source and instruct the model to say 'I don't know' when retrieval returns nothing relevant. Require citation/attribution for every claim and reject output that can't be traced to a retrieved chunk. For code assistants, check suggested package names against a verified registry allowlist before install, killing the slopsquatting path. Add a visible 'verify before use' disclaimer in regulated domains. Limits: RAG only helps if the corpus actually covers the question, and it does not stop the model from mixing retrieved facts with confident invention on the gaps.
Which vendors cover LLM09 Misinformation?
| Vendor | Category | Coverage | How it's addressed | Source |
|---|---|---|---|---|
| Lakera | Guardrails / LLM Firewall | Covered | Content moderation and factuality evaluation capabilities. | https://www.lakera.ai/blog/owasp-top-10-for-llm-applications-lakera-alignment |
| Straiker | Agentic AI Security | Covered | Defend AI's detection engine runs specialized detectors for hallucination alongside prompt injection, tool misuse, and code-injection patterns as a core guardrail category. | https://www.straiker.ai/products/defend-ai |
| Vijil | Agentic AI Security | Covered | Hallucination detection is a stated evaluation dimension. | https://vijil.ai/ |
| Enkrypt AI | Guardrails / LLM Firewall | Covered | Detects and monitors hallucinations and model drift; LLM Safety Leaderboard benchmarks robustness. | https://www.enkryptai.com/ |
| Giskard | AI Red Teaming | Covered | Hallucination, sycophancy, contradiction and omission detection. | https://www.giskard.ai/ |
| Mindgard | AI Red Teaming | Covered | DAST-AI platform is documented as surfacing real-world misuse patterns including hallucinations as part of adversarial red-team testing. | https://mindgard.ai/blog/what-is-ai-red-teaming |
| NeuralTrust | Guardrails / LLM Firewall | Covered | 'Hallucination' is explicitly listed in TrustTest's documented taxonomy of tested failure modes/attacks. | https://neuraltrust.ai/red-teaming |
| Pangea | Guardrails / LLM Firewall | Covered | AI Guard detects and disarms malicious/harmful content in interactions. | https://www.securityweek.com/pangea-launches-ai-guard-and-prompt-guard-to-combat-gen-ai-security-risks/ |
| Noma Security | AI-SPM / Runtime Protection | Partial | Noma's agentic-risk documentation describes behavioral analytics/anomaly detection that flags deviations to help 'defend against cascading hallucinations and rogue agents' as part of runtime protection, though not framed as a dedicated hallucination-detection feature. | https://noma.security/agentic-security/series-3-addressing-agentic-risk/blog-3-2-addressing-agentic-risk-part-2/ |
| TrojAI | AI Red Teaming | Partial | Detect flags unwanted/harmful content; explicit misinformation/hallucination scope unconfirmed. | https://troj.ai/products/detect |
| Akto | Agentic AI Security | Partial | Lists hallucination risk among covered attack surface. | https://www.akto.io/agentic-security |
| Robust Intelligence | AI Red Teaming | Partial | Validation tests for hallucination/unsafe content. | https://sequoiacap.com/article/robust-intelligence-spotlight/ |
| WitnessAI | Guardrails / LLM Firewall | Partial | Filters harmful/inappropriate responses; not a dedicated hallucination detector. | https://witness.ai/ |
| CalypsoAI | AI Red Teaming | Partial | Guardrails and leaderboard assess harmful/unsafe model outputs. | https://calypsoai.com/news/redefining-ai-security-calypsoai-security-leaderboard-inference-red-team/ |
| Credo AI | AI Governance / Model Risk | Partial | Risk Intelligence treats reliability/misinformation as a governed risk category, without hallucination detection. | https://www.credo.ai/ |
4 vendors score Not covered and 2 score Unverified for LLM09 — see the full coverage matrix.
Open-source tools related to Misinformation
These open-source projects also address misinformation, though we track them by repository health rather than scoring them like vendors:
- garak — LLM vulnerability scanner — automated probing for jailbreaks, prompt injection, data leakage, hallucination, and other failure modes
- Giskard — Open-source evaluation and testing library for LLM agents/RAG — detects hallucination, bias, prompt injection, and other quality/security issues
- promptfoo — Prompt/agent/RAG testing and LLM red-teaming — vulnerability scanning, eval, and CI/CD security testing for AI applications
- PyRIT — Python Risk Identification Tool — open-source framework for red-teaming/risk-identification of generative AI systems
Frequently asked questions
Which vendors cover LLM09 Misinformation?
8 of the 21 vendors llmthreat tracks score Covered for LLM09 Misinformation: Lakera, Straiker, Vijil, Enkrypt AI, Giskard, and others. 7 more score Partial, as of 2026-07-13.
What is LLM09 Misinformation?
The model produces false or fabricated content that users act on.