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Security audit

Stuck Trace

Security checks for vulnerabilities and agentic risk

Overview

The skill is not clearly malicious, but it should be reviewed because it automatically reads broad personal memory, profile, and recent conversation data to produce blocker analysis.

Install only if you are comfortable with the agent using profile files, memory archives, and recent conversations to analyze project blockers. Prefer running it only after approving the exact sources to read, and avoid use in workspaces containing private logs, medical, financial, identity, or sensitive relationship records unless those are excluded before access.

Vulnerability Patterns
  • Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs
  • Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
  • 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 (1)

T05 · Unauthorized Access and Privilege Escalation

Warning
Location
SKILL.md:81
Finding

Excessive Access to Sensitive Agent Profiles, Memory, and Conversation History

Content
View full analysis
` blocks and the `memory/private/` directory ``` The snippet is an English rendering of the relevant instructions at the cited location. ### Technical Analysis The Skill directs the host agent to load broad classes of sensitive information before performing project-blocker analysis. These sources include personality profiles, relationship and pressure information, persistent memory, recent conversation history, and content explicitly designated as private. The stated output restrictions only prevent direct disclosure. They do not prevent private information from being read, placed into model context, processed during inference, retained in host telemetry, or indirectly reflected in conclusions. In particular, allowing information from `memory ...[truncated 2314 chars]
Remediation
View remediation
` blocks, credentials, identity records, medical records, or financial records. These exclusions must apply at the access stage, not only at output generation. 3. Use a strict source allowlist limited to user-selected project files and conversations directly associated with the named project. 4. Replace recursive memory searches with bounded searches over explicit paths, project identifiers, and narrow date ranges. 5. Display the proposed source list before access and allow the user to approve or reject each source. 6. Record which sources were accessed and disclose that list in the result without exposing their sensitive contents. 7. Minimize context retention and disable storage or telemetry for retrieved sensitive material where the host supports those controls. 8. Treat missing consent or ambiguous project scope as a reason to use the existing zero-record mode. 9. Add automated tests confirming that private directories, private-marked blocks, and unrelated records are never opened or searched. 10. Apply redaction before information enters the model context rather than relying solely on post-generation output filtering. ]]>
Vulnerability Patterns
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Findings (6)

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
91% confidence
Finding

The trigger phrases include broad terms like 'blocked', 'root cause', and common Chinese phrases that can appear in ordinary conversation. This can cause unintended activation of a skill that reads SOUL.md, USER.md, memory, and recent conversations, leading to over-collection and unexpected synthesis of sensitive context when the user did not intend to invoke that behavior.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
97% confidence
Finding

The skill says it will read profile, memory, and recent conversation files, but it does not present an upfront user-facing warning or consent step before doing so. In a context involving personal archives and recent chats, silent access is dangerous because users may not realize the breadth of data being pulled into analysis, increasing privacy and confidentiality risk.

Content

No source excerpt is available for this finding.

Ssd 3

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The skill explicitly instructs the agent to read user archives, memory, and recent conversations, then synthesize an output from all prior user-provided data. Even with privacy-boundary rules, this creates a semantic data-leak risk because sensitive facts can be paraphrased, inferred, or exposed to unintended viewers through the generated summary, especially in organizational and interpersonal contexts.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
89% confidence
Finding

The manifest and boundary sections say the skill should not give advice or substitute for decision-making. However, the required output format includes a section '还能走的路' listing candidate paths and marking one as '已关闭', which goes beyond neutral tracing into option recommendation or elimination; the demo further concretizes those prescriptions.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
94% confidence
Finding

The documentation repeatedly says the skill should describe only the structure of the current blockage, must not imply causality, and must not say generalized things like '你总是' or diagnose recurring personal patterns. But the Demo section presents exactly that kind of output, e.g. '你每次都在节点 2 让步' and '这是你第三次在...模式上卡住', which instructs the model toward cross-episode causal/pattern judgments the earlier rules forbid.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Low
Category
Not specified by scanner
Confidence
84% confidence
Finding

The visible display name, description, trigger guidance, and user interaction examples are primarily in Chinese, and the skill instructs specific Chinese trigger phrases without indicating that other languages are equally supported by default. This can create an implicit language policy constraint without user choice.

Content

No source excerpt is available for this finding.