building-adversary-infrastructure-tracking-system
Build an automated system to track adversary infrastructure using passive DNS, certificate transparency, WHOIS data, and IP enrichment to map and monitor threat actor command-and-control networks.
52
58%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Risky
Do not use without reviewing
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/building-adversary-infrastructure-tracking-system/SKILL.mdSecurity
2 findings — 1 high severity, 1 medium severity. You should review these findings carefully before considering using this skill.
W007: Insecure credential handling detected in skill instructions
What this means
The skill handles credentials insecurely by requiring the agent to include secret values verbatim in its generated output. This exposes credentials in the agent’s context and conversation history, creating a risk of data exfiltration.
Why it was flagged
Insecure credential handling detected (high risk: 0.90). The prompt includes explicit API key placeholders and code that injects those keys directly into HTTP request headers and instantiated objects, which encourages providing and embedding secret values verbatim in generated code/requests (exfiltration risk).
W011: Third-party content exposure detected (indirect prompt injection risk)
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
Third-party content exposure detected (high risk: 0.75). The required runtime workflow calls external APIs (e.g., `query_crtsh()` to `https://crt.sh/?...&output=json` and `query_urlhaus()` / `query_threatfox()`), and the returned JSON fields (like certificate SANs / domain names and other text values) are ingested into the agent’s in-memory structures and then into the generated report, which would be passed into the LLM context as free-form text if the agent uses that report for prompting—these are outsider-authored public web/API content.
- Repository
- mukul975/Anthropic-Cybersecurity-Skills
- Commit
0445030
- Audited
- Security analysis
Is this your skill?
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