CtrlK
BlogDocsLog inGet started
Tessl Logo

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.

69

Quality

85%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Critical

Do not install without reviewing

SKILL.md
Quality
Evals
Security

Quality

Content

96%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A dense, highly actionable offensive-security skill body that is token-efficient and pairs a clear audit workflow with an explicit finding-validation contract. The only minor gap is progressive disclosure: it is a single long file that could offload some payload/CVSS detail to references.

DimensionReasoningScore

Conciseness

Lean and token-efficient: it assumes Claude knows what XSS/SQLi/SSTI are and spends tokens on payloads, grep commands, and tables rather than concept explanations; every section earns its place.

5 / 5

Actionability

Fully executable guidance throughout — copy-paste grep recipes, concrete attack payloads, ready-to-send PoC prompts, and explicit CVSS vectors covering the common cases.

5 / 5

Workflow Clarity

Sequenced audit workflow (find sinks via grep → ask 3 validation questions per sink → exploit → validate) with an explicit validate_finding contract providing success/negative patterns and a negative_command feedback loop.

5 / 5

Progressive Disclosure

Well-organized into 8 clearly headed sections with no nested references and no bundle files to mismanage, but at ~155 lines some material (full PoC payload set, CVSS table) is inline rather than split into reference files.

4 / 5

Total

19

/

20

Passed

Description

75%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A specific, well-scoped security description with strong action and outcome coverage and low conflict risk. Its main weakness is the absence of an explicit 'Use when...' trigger clause in the description field itself.

Suggestions

Add an explicit 'Use when...' clause to the description, e.g. 'Use when auditing agentic or chat systems that pass model output to renderers, shells, SQL, or templates without sanitisation.'

Soften the purely technical trigger 'OWASP LLM05:2025' by pairing it with a natural phrase users would say, such as 'LLM output handling flaws' or 'model output sanitisation'.

Consider surfacing the metadata.when_to_use trigger phrasing directly in the description so the when-guidance is visible at first glance.

DimensionReasoningScore

Specificity

Names the domain and multiple concrete actions ('renders / executes / shells it without sanitisation') plus a comprehensive list of outcomes (XSS, SSRF, SQL injection, RCE, SSTI), matching the comprehensive-coverage anchor.

5 / 5

Completeness

The 'what' is clear ('Hunt improper LLM output handling... producing XSS...') but there is no explicit 'Use when...' clause in the description field, capping completeness at 3 per the missing-trigger-guidance rule.

3 / 5

Trigger Term Quality

Good keyword coverage including 'LLM output handling', 'OWASP LLM05', 'XSS', 'SSRF', 'SQL injection', 'RCE', 'SSTI' that a security user would say, though 'OWASP LLM05:2025' leans technical and a few natural phrasings are absent.

4 / 5

Distinctiveness Conflict Risk

Clear niche (LLM05 improper output handling via the model channel) explicitly distinguished from sibling OWASP LLM entries, with distinct triggers and minimal conflict risk.

5 / 5

Total

17

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

relative_links

Relative link issues: 2 suspicious

Warning

Total

14

/

16

Passed

Repository
PurpleAILAB/Decepticon
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.