SKILLEMALL.ai

CC exploiting-insecure-deserialization

Identifying and exploiting insecure deserialization vulnerabilities in Java, PHP, Python, and .NET applications to achieve remote code execution during authorized penetration tests.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 2 798 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Identifying and exploiting insecure deserialization vulnerabilities in Java, PHP, Python, and .NET applications to achieve remote code execution during…

As a process C 62/100 · Has gaps — weak spots: when it triggers, failures and branches, progress reporting

ProcedureGitHubCommerceSecuritytype and topics are labelled automatically from the skill text
JSON
Technical rating
C
86/100
safety, quality, tests
Safety 60%
92
Quality 40%
78
Run on models
none yet
Process rating
C
62/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 8

    ✓ No critical or high findings

    Medium and low: 8
    • low Risky intent intent-offensive-security references/api-reference.md:8
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | pickle | stdlib | Python pickle payload generation for testing |
    • low Risky intent intent-offensive-security SKILL.md:42
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **Authorization**: Written penetration testing agreement with RCE testing scope
    • low Risky intent intent-offensive-security SKILL.md:323
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - Internal network access confirmed via reverse shell

    A further 5 matches are quotations in this security skill's documentation and are not counted as findings.

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "domain"
    • note frontmatter-key unknown frontmatter key "subdomain"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"

    Process rating: all ten parameters 62/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (git, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2798 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)
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 181: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 12 items
    • +3Output format is stated explicitly
    • +4Has examples (8 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.