analyzing-cloud-storage-access-patterns
Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules.
72
90%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Low
Low-risk findings worth noting
Low
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
In `scripts/agent.py`, the runtime workflow executes `aws cloudtrail lookup-events` in `query_cloudtrail_s3_events()` and then reads/ingests the returned CloudTrail `CloudTrailEvent` JSON fields (e.g., `sourceIPAddress`, `requestParameters.bucketName/key`, `userIdentity.arn`) from that external log content into `s3_events` for anomaly detection.
- Repository
- mukul975/Anthropic-Cybersecurity-Skills
- Commit
e612f49
- Audited
- Security analysis
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.