analyzing-campaign-attribution-evidence
Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.
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Low
Low-risk findings worth noting
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tessl review fix ./skills/analyzing-campaign-attribution-evidence/SKILL.mdLow
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
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
The runtime workflow in scripts/agent.py ingests outsider-authored free text only via user-provided evidence inputs (e.g., evidence["strings"] passed to evaluate_language_artifacts via evaluate_language_artifacts at line 129-146) rather than reading from any third-party queue/feed/chat/comment source.
- Repository
- mukul975/Anthropic-Cybersecurity-Skills
- Commit
e612f49
- Audited
- Security analysis
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