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CB testing-for-sensitive-data-exposure

Identifying sensitive data exposure vulnerabilities including API key leakage, PII in responses, insecure storage, and unprotected data transmission during security assessments.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 3 409 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Identifying sensitive data exposure vulnerabilities including API key leakage, PII in responses, insecure storage, and unprotected data transmission during…

As a process B 68/100 · Nearly there — weak spots: when it triggers, progress reporting

IntegrationAWSGitHubDockerStripeData and analyticsSecuritytype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
84/100
safety, quality, tests
Safety 60%
88
Quality 40%
78
Run on models
none yet
Process rating
B
68/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Failures and branches w 10
55
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Secrets in code medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.

For the author

The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.

How to improve

    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 · 8

    ✓ No critical or high findings

    Medium and low: 8
    • medium Secrets in code secret-private-key references/api-reference.md:22
      Private key material (key header without key body; documentation table row)
      | Private Key | `-----BEGIN PRIVATE KEY----- …
      header onlytable
    • low Secrets in code secret-aws-key references/api-reference.md:18
      AWS access key ID (placeholder value)
      | AWS Access Key | `AKIA[0-9A-Z]{16}` | AKIA…PLE |
      placeholder
    • low Risky intent intent-offensive-security SKILL.md:51
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **Authorization**: Written penetration testing agreement with data handling scope
    • low Exfiltration net-credential-use SKILL.md:110
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s -H "Authorization: Bearer $TOKEN" \
      security skill
    • low Exfiltration net-credential-use SKILL.md:123
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s -H "Authorization: Bearer $TOKEN" \
      security skill
    • low Exfiltration net-credential-use SKILL.md:130
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s -H "Authorization: Bearer $TOKEN" \
      security skill
    • low Exfiltration net-credential-use SKILL.md:141
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s -H "Authorization: Bearer $TOKEN" \
      security skill
    • low Exfiltration net-credential-use SKILL.md:260
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s -H "Authorization: Bearer $TOKEN" \
      security skill

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "domain"
    • note frontmatter-key unknown frontmatter key "subdomain"
    • note frontmatter-key unknown frontmatter key "nist_ai_rmf"
    • note frontmatter-key unknown frontmatter key "atlas_techniques"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"

    Process rating: all ten parameters 68/100

    • 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
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3409 tokens
    • 100Running it twice. Mutating operations check current state

    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)
    • -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 177: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 12 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.