T01 · Skill Instruction Hijacking
Error
- Location
SKILL.md:116- Finding
Untrusted Corpus Instructions Can Be Promoted to Executable Agent Tasks
- Content
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- Remediation
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Security audit
Batch Executor
Security checks for vulnerabilities and agentic risk
Overview
The skill has a coherent batch-processing purpose, but it automatically executes tasks found in private corpus dumps while saving and committing raw content, so it needs careful review before use.
Install only if you are comfortable with an agent processing and acting on large private document exports. Before use, require an explicit execution plan, keep raw corpora out of git, avoid sensitive sources unless redacted, and approve any sub-agent, file mutation, API call, or commit that comes from imported content.
Vulnerability Patterns
- Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
- Insecure Skill Coding PracticesFinds exploitable flaws such as hardcoded secrets or command injection
- Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
- Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review
- Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally
Findings (2)
T09 · Insecure Skill Coding Practices
Warning
- Location
SKILL.md:134- Finding
Sensitive Raw Corpora May Be Persisted in Plaintext and Captured in Git History
- Content
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- Remediation
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Vulnerability Patterns
- Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
- Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
- Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
- Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
- Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
Findings (5)
Missing User Warnings
Medium
- Category
- Not specified by scanner
- Confidence
- 96% confidence
- Finding
The skill explicitly requires saving all raw corpus input to disk before any processing, but provides no safeguards for secrets, personal data, regulated content, or user consent. In the context of Google Drive dumps, ChatGPT exports, and Apple Notes, this creates a substantial privacy and data-retention risk because highly sensitive data may be stored locally in plain form unnecessarily.
- Content
Ssd 3
Medium
- Category
- Not specified by scanner
- Confidence
- 97% confidence
- Finding
This workflow mandates retaining raw corpus content and then redistributing item content and related context to downstream processing stages. For data sources like conversation exports, notes, and drive dumps, that materially increases the attack surface for natural-language leakage, including exposure of credentials, personal data, internal documents, and sensitive instructions to sub-agents or logs that do not need the full raw text.
- Content
Ssd 3
Medium
- Category
- Not specified by scanner
- Confidence
- 95% confidence
- Finding
The skill instructs broad propagation of corpus contents into manifests, execution context, status tracking, reports, and later learning artifacts, which multiplies the number of plaintext copies and derivative disclosures. In a corpus-scale executor, this is especially dangerous because the source material is likely heterogeneous and may contain confidential business data, private messages, or regulated information that should not be repeated across multiple files and agents.
- Content
Missing User Warnings
Medium
- Category
- Not specified by scanner
- Confidence
- 94% confidence
- Finding
The instruction to commit progress to git can permanently record corpus-derived content in repository history, making accidental disclosure much harder to undo than a normal file write. Because this skill operates on large personal or organizational exports, even brief manifest entries, checkpoints, or generated artifacts may embed sensitive information that then propagates into version control and backups.
- Content
Missing User Warnings
Low
- Category
- Not specified by scanner
- Confidence
- 90% confidence
- Finding
Appending outputs to shared files such as a value stack and learnings log spreads corpus-derived information beyond the original processing boundary without notice or isolation. This increases the chance that sensitive details from one batch are later exposed to unrelated workflows, users, or skills through shared context files.
- Content