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Security audit

continue-learning

Security checks across malware telemetry and agentic risk

Overview

This skill analyzes OpenClaw session history to produce bounded learning suggestions, with disclosed storage and deletion controls, but users should review its learning/auto-apply language carefully.

Install only if you want local session-history analysis and persistent learning summaries. Use --agent for narrow scope, reserve --all for intentional broad review, periodically inspect or prune memory/learning, and do not let high-confidence suggestions change agent behavior without your review.

SkillSpector

By NVIDIA
Vulnerability Patterns
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • 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 (3)

Vague Triggers

Medium
Confidence
91% confidence
Finding
The trigger list is overly broad and matches common, high-level concepts such as 'pattern detection', 'session analysis', and 'agent optimization'. That can cause the skill to activate in contexts where the user did not intend self-modification or behavioral learning, increasing the chance of unnecessary session analysis and unintended persistence of inferred preferences.

Natural-Language Policy Violations

Medium
Confidence
94% confidence
Finding
The skill states that higher-confidence patterns may be 'Auto-approved' or 'Always apply', which implies behavior changes can occur without fresh user consent. Even if framed as optimization, this creates a risk that the agent silently adapts communication style, workflow, or decision-making in ways the user did not request and may not notice immediately.

Autonomous Decision Making

Medium
Category
Excessive Agency
Content
|-------|---------|----------|
| 0.3 | Tentative | Suggested but not enforced |
| 0.5 | Moderate | Applied when relevant |
| 0.7 | Strong | Auto-approved |
| 0.9 | Core behavior | Always apply |

**Confidence increases when:**
Confidence
90% confidence
Finding
The 'Auto-approved' behavior is a form of autonomous decision-making that allows the system to change how it responds based on internally derived patterns rather than explicit current instructions. In a self-improvement skill, that is particularly sensitive because mistakes in inferred preferences can propagate and become entrenched across future interactions.

VirusTotal

65/65 vendors flagged this skill as clean.

View on VirusTotal

Static analysis

No suspicious patterns detected.