BB ffuf-web-fuzzing
Expert guidance for ffuf web fuzzing during penetration testing, including authenticated fuzzing with raw requests, auto-calibration, and result analysis
Expert guidance for ffuf web fuzzing during penetration testing, including authenticated fuzzing with raw requests, auto-calibration, and result analysis
As a process B 67/100 · Nearly there — weak spots: result and completion, when it triggers, failures and branches
The same skill appears in 2 more places: agentic-awesome-skills, agentic-awesome-skills
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
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 · 9
✓ No critical or high findings
Medium and low: 9
-
low Risky intent
intent-offensive-securityreferences/detailed-guide.md:6Offensive-security / dual-use content (legitimate for authorised testing; review intended use)FFUF is a fast web fuzzer written in Go, designed for discovering hidden content, directories, files, subdomains, and testing for vulnerabilities during penetration testing. It's significantly faster
-
low Risky intent
intent-offensive-securityreferences/detailed-guide.md:293Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)Use `-ac` by default for every scan. This is non-negotiable for productive pentesting:
detector -
low Risky intent
intent-offensive-securityreferences/detailed-guide.md:492Offensive-security / dual-use content (legitimate for authorised testing; review intended use)1. **ALWAYS include `-ac` in every command** - This is mandatory for productive pentesting and result analysis
-
low Risky intent
intent-offensive-securityreferences/detailed-guide.md:504Offensive-security / dual-use content (legitimate for authorised testing; review intended use)10. For pentesting reports, use `-of html` or `-of csv` for client-friendly formats
-
low Risky intent
intent-offensive-securitySKILL.md:3Offensive-security / dual-use content (legitimate for authorised testing; review intended use)description: Expert guidance for ffuf web fuzzing during penetration testing, including authenticated fuzzing with raw requests, auto-calibration, and result analysis
-
low Risky intent
intent-offensive-securitySKILL.md:30Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)- You are fuzzing web targets with `ffuf` during authorized security testing or penetration testing.
detector
A further 3 matches are quotations in this security skill's documentation and are not counted as findings.
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "risk" - note
frontmatter-keyunknown frontmatter key "source" - note
frontmatter-keyunknown frontmatter key "date_added"
Process rating: all ten parameters 67/100
- 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
- 40Result and completion. Does not say what the result is
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 6 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 469 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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 153: enough signal without eating the budget
- +4Structure: 4 headings
- +3Step-by-step instructions: 6 items
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.