BD hunt-idor
Hunting skill for idor vulnerabilities. Built from 26 public bug bounty reports. Use when hunting idor on any target.
Hunting skill for idor vulnerabilities.
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
ProcedureShopifyData and analyticsSecurityCommercetype and topics are labelled automatically from the skill text
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 5
✓ No critical or high findings
Medium and low: 5
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low Risky intent
intent-offensive-securitySKILL.md:3Offensive-security / dual-use content (legitimate for authorised testing; review intended use)description: Hunting skill for idor vulnerabilities. Built from 26 public bug bounty reports. Use when hunting idor on any target.
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low Risky intent
intent-offensive-securitySKILL.md:12Offensive-security / dual-use content (legitimate for authorised testing; review intended use)- Chains easily with privilege escalation, financial fraud, and account takeover
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low Risky intent
intent-offensive-securitySKILL.md:22Offensive-security / dual-use content (legitimate for authorised testing; review intended use)| Account management endpoints | User addition, deletion, privilege escalation (PayPal, Mozilla) |
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low Risky intent
intent-offensive-securitySKILL.md:113Offensive-security / dual-use content (legitimate for authorised testing; review intended use)- IDOR → leaked ID → second IDOR → privilege escalation
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low Risky intent
intent-offensive-securitySKILL.md:351Offensive-security / dual-use content (legitimate for authorised testing; review intended use)- **Impact:** Cross-tenant privilege escalation via IDOR + mass assignment. The single most efficient takeover chain on SaaS team-management APIs.
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5275 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "sources" - note
frontmatter-keyunknown frontmatter key "report_count" - note
edit-residuethe text marks something as outdated (lines 379): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 46/100
- 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
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 70Execution cost. Instruction body is 5275 tokens
- 100Steps. 131 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- low 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (4 tags): a typed call is more reliable
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 117: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 17 headings
- +3Step-by-step instructions: 131 items
- +4Has examples (11 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.