SKILLEMALL.ai

BC owasp-top-10-testing

Test an application against the OWASP Top 10 with Strix — autonomous AI agents that attempt real exploits for each category of the current OWASP Top 10:2025 (broken access control including SSRF, security misconfiguration, software supply chain failures, cryptographic failures, injection, insecure design, authentication failures, integrity failures, logging and alerting failures, mishandling of exceptional conditions) and report only what they could actually prove, mapped back to the category with a proof-of-concept. Also covers the OWASP API Security Top 10 (2023). Use when the user asks for an OWASP Top 10 assessment, OWASP compliance testing, or a security review mapped to OWASP categories.

usestrix/strix Agent Skills author: usestrix Apache-2.0 1 file body ≈ 1 343 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Test an application against the OWASP Top 10 with Strix — autonomous AI agents that attempt real exploits for each category of the current OWASP Top 10:2025…

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerSecuritytype and topics are labelled automatically from the skill text
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
96
Quality 40%
85
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Risky intent intent-offensive-security SKILL.md:16
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      Install, LLM setup, and the managed-cloud alternative: **penetration-testing-with-strix**. For a run with no Docker and no LLM key, the same binary drives the managed platform: `strix cloud login`, th
      detector
    • low Risky intent intent-offensive-security SKILL.md:24
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | A01 Broken Access Control (incl. SSRF) | **Strong** — cross-user/tenant access, privilege escalation, IDOR, and SSRF (including blind, via out-of-band callbacks) are all exploit-validated. Needs two
    • low Risky intent intent-offensive-security SKILL.md:48
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      Prioritise A01 (cross-org access, privilege escalation, SSRF), A02, A05, A07, A10.
      detector
    • low Risky intent intent-offensive-security SKILL.md:54
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - Need an auditor-facing PDF? Run it through the managed platform and pull the technical report (**managed-pentesting-with-strix**).

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 59/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 75Steps. 3 steps
    • 100Tools and files. No external tools needed
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1343 tokens
    • 100Running it twice. No mutating operations

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +3Description length 702: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 3 items
    • +4Has examples (1 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.