CC performing-jwt-none-algorithm-attack
Execute and test the JWT none algorithm attack, crafting tokens with the alg header set to none using PyJWT and an intercepting proxy (Burp Suite/mitmproxy) to bypass signature verification and forge arbitrary claims. Use during authorized penetration tests or security assessments of applications that use JWT for authentication or authorization, to validate that the server rejects unsigned tokens.
Execute and test the JWT none algorithm attack, crafting tokens with the alg header set to none using PyJWT and an intercepting proxy (Burp Suite/mitmproxy)…
As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, progress reporting
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 · 4
✓ No critical or high findings
Medium and low: 4
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:69High-entropy token-like string (may be an id, hash or a credential)SflK…w5c # Signature
-
low Obfuscation
obf-base64-blobSKILL.md:79Long base64-looking blob (documentation of a security skill)Authorization: Bearer eyJh…w5c
security skill -
low Risky intent
intent-offensive-securitySKILL.md:347Offensive-security / dual-use content (legitimate for authorised testing; review intended use)- HackTricks JWT Vulnerabilities: https://book.hacktricks.xyz/pentesting-web/hacking-jwt-json-web-tokens
A further 1 matches are quotations in this security skill's documentation and are not counted as findings.
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "domain" - note
frontmatter-keyunknown frontmatter key "subdomain" - note
frontmatter-keyunknown frontmatter key "nist_csf" - note
frontmatter-keyunknown frontmatter key "mitre_attack"
Process rating: all ten parameters 59/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 22 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2901 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
- -41 reference files, but SKILL.md never points to them: the model will not open them
- -31 of 1 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 400: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 22 items
- +4Has examples (6 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.