Pivot from SSRF or RCE to cloud Instance Metadata Service (IMDS) — extract IAM role creds, instance identity, user-data secrets.
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Critical
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tessl review fix ./packages/decepticon/decepticon/skills/standard/cloud/imds-pivot/SKILL.mdSecurity
2 findings: 1 critical severity, 1 medium severity. Installing this skill is not recommended: please review these findings carefully if you do intend to do so.
Detected high-risk code patterns in the skill content — including its prompts, tool definitions, and resources — such as data exfiltration, backdoors, remote code execution, credential theft, system compromise, supply chain attacks, and obfuscation techniques.
This document is an explicit offensive playbook for abusing SSRF/RCE to access cloud instance metadata, steal service credentials/tokens, and pivot — high-risk malicious guidance for credential theft and data exfiltration.
The skill prompts the agent to compromise the security or integrity of the user’s machine by modifying system-level services or configurations, such as obtaining elevated privileges, altering startup scripts, or changing system-wide settings.
This skill explicitly instructs accessing instance metadata endpoints and extracting cloud credentials (writing them to /tmp, exporting them, and using them) and describes SSRF/IMDS bypass techniques — actions that enable active credential theft and remote pivoting from the host.
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
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.
The workflow is an SSRF/instance-metadata pivot that at runtime would fetch cloud metadata URLs (e.g., AWS/GCP/Azure 169.254.169.254 and metadata.google.internal) and the responses (IAM creds, user-data, access tokens) are arbitrary plain text originating from the target environment, which then becomes readable LLM context via whatever tool/agent execution captures and presents the fetched content.
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