AD hunt-api-misconfig
Hunt API security misconfiguration — mass assignment, prototype pollution, HTTP verb tampering. Mass assignment: send {is_admin:true, role:admin, verified:true} on profile/account/reset endpoints — server blindly applies. JWT signature/crypto forging (alg:none, key confusion, kid/jku) is owned by hunt-jwt-crypto; this skill covers only non-crypto JWT handling. Prototype pollution: __proto__ injection in JSON merge / Object.assign / lodash _.merge → polluted prototype reaches sink (RCE in Node, XSS in browser). HTTP verb: GET-bypass-CSRF, X-HTTP-Method-Override, TRACE enabled. Detection: API responses with extra fields, JWTs in headers (decode at jwt.io). CORS misconfiguration (reflect-any-origin, null origin, subdomain-regex bypass, postMessage) is owned by hunt-cors. Use when hunting API misconfigs, mass-assignment, prototype pollution (JWT crypto → hunt-jwt-crypto).
Hunt API security misconfiguration — mass assignment, prototype pollution, HTTP verb tampering.
As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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-securitySKILL.md:12Offensive-security / dual-use content (legitimate for authorised testing; review intended use)User.update(req.body) // body has {"role": "admin"} → privilege escalation
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "sources" - note
frontmatter-keyunknown frontmatter key "report_count" - note
edit-residuethe text marks something as outdated (lines 231): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 42/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 7 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 70Execution cost. Instruction body is 4212 tokens
- 100Steps. 32 steps
- 100Consistency. Name and required fields are in place
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)
- +3Description length 880: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 21 headings
- +3Step-by-step instructions: 32 items
- +4Has examples (16 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.