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AC hunt-deserialization

Hunt Insecure Deserialization — Java gadget chains (ysoserial), PHP object injection (phpggc), Python pickle RCE, .NET BinaryFormatter, Ruby Marshal.load, JNDI/Log4Shell. RCE via deserialization is almost always Critical. Use when target runs Java, PHP serialization, Python pickle, .NET, or Ruby on Rails.

elementalsouls/Claude-BugHunter Agent Skills author: elementalsouls 1 file body ≈ 1 413 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Hunt Insecure Deserialization — Java gadget chains (ysoserial), PHP object injection (phpggc), Python pickle RCE, .NET BinaryFormatter, Ruby Marshal.load…

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ProcedureGitHubSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
82
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Risky intent intent-offensive-security SKILL.md:156
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      | RCE as low-privilege user | Find SUID binaries / sudo rules | Privilege escalation → root |
      detector

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "sources"
    • note frontmatter-key unknown frontmatter key "report_count"

    Process rating: all ten parameters 54/100

    • 0Result and completion. Does not say what the result is
    • 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 (bash, web, python, node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 9 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1413 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 306: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (10 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.