Individual skill
improper-output-handling
Hunt improper LLM output handling (OWASP LLM05:2025) — downstream code that trusts unstructured model output and renders / executes / shells it without sanitisation, producing XSS, SSRF, SQL injection, RCE, and SSTI via the model channel.
packages/decepticon/decepticon/skills/standard/analyst/improper-output-handling/SKILL.md
Source commit df879550544d36b6b1e69062f05adae28537adf2
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- Observed behavior
- Not checked yet
- Last checked
- Nov 8, 2026
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Repository context
- Repository stars (not skill ratings)
- 6,567
- Repository last updated
- Nov 7, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent Repository topic
- ai Repository topic
- cybersecurity Repository topic
- hacking Repository topic
- langchain Repository topic
- langgraph Repository topic
- llm Repository topic
- generative-ai Repository topic
- pentesting Repository topic
- pentest Repository topic
- data Derived category
- security Derived category
- data-databases Derived category
- security-application Derived category
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# LLM Improper Output Handling (LLM05:2025)
The LLM is an attacker-influenced source. Every byte it emits should
be treated like raw HTTP input. Most products in 2026 still don't,
and the result is a classic web-app vuln class wrapped in an LLM
delivery vehicle: model output renders as HTML, gets concatenated
into a shell command, or is interpolated into a SQL query.
## 1. Recognition signals
- Chat interface renders Markdown / HTML / images from the model.
- Agentic system passes model output to ``subprocess`` / ``exec`` /
``eval`` / ``os.system``.
- Tool wrappers concatenate model output into URLs, SQL strings, or