behavioral-invariant-monitor
Helps verify that AI agent skills maintain consistent behavioral invariants across repeated executions — detecting the class of threat where a skill behaves safely during initial evaluation but shifts behavior based on execution count, environmental conditions, or delayed activation triggers. v1.3 adds performance fingerprinting (computational complexity drift detection), cryptographic audit trails (hash-chained behavior logs for immutable verification), and risk-proportional monitoring (sampling-based checks to reduce overhead).
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/andyxinweiminicloud/behavioral-invariant-monitorWhat This Skill Does
The behavioral-invariant-monitor is a sophisticated security utility for the OpenClaw ecosystem designed to solve the 'delayed activation' threat vector. Many malicious or buggy agent skills are designed to pass initial sandbox evaluations by maintaining safe, expected behavior during the first few runs, only to trigger malicious logic after a set number of execution cycles, a specific date, or when it detects a production-grade execution environment. This monitor provides a persistent auditing layer that watches for deviations in behavioral patterns, resource utilization, and operational fingerprints across the entire lifecycle of an installed skill.
v1.3 introduces powerful advancements including performance fingerprinting, which detects computational complexity drift, and cryptographic audit trails that leverage hash-chained logs to ensure that your behavioral history cannot be tampered with by the monitored skill itself. By focusing on immutable behavioral invariants—such as expected output structures, consistent network patterns, and resource consumption bounds—it provides high-fidelity monitoring without the extreme overhead of full-output, exhaustive sandbox re-testing.
Installation
To install this skill, use the ClawHub CLI command:
clawhub install openclaw/skills/skills/andyxinweiminicloud/behavioral-invariant-monitor
Use Cases
- Supply Chain Security: Verify that third-party skills maintain stable resource footprints and output patterns after updates or across long-term production use.
- Anomaly Detection: Catch 'logic bombs' or time-gated payloads that only execute after a specific number of interactions with the agent.
- Performance Auditing: Ensure that skills do not secretly begin exfiltrating data or performing heavy background computation in later stages of operation.
- Compliance Monitoring: Maintain a cryptographically verifiable audit trail of how sensitive skills behave across varying environments.
Example Prompts
- "Analyze the behavioral logs for the past 50 executions of my 'data-processor-v2' skill and check for any computational drift or resource usage anomalies."
- "Enable high-frequency sampling for the behavioral-invariant-monitor on all installed third-party utilities to detect potential N-run delay patterns."
- "Show me the cryptographic audit report for the 'web-scraper-pro' skill to verify that its output invariants have remained consistent since installation."
Tips & Limitations
- Resource Overhead: While the monitor uses sampling-based checks to reduce computational load, enabling monitoring on every single skill may slightly increase total system latency. Prioritize monitoring for high-privilege or third-party skills.
- Baseline Establishment: The monitor requires a brief 'learning period' to establish what constitutes a 'normal' behavioral fingerprint for a skill. Avoid flagging alerts during the first 5-10 runs while the monitor calibrates.
- Invariant Definition: Ensure that you understand the expected behavior of your skills; if a skill is non-deterministic by design (e.g., AI creative writing), set the determinism sensitivity threshold lower to avoid false positives.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-andyxinweiminicloud-behavioral-invariant-monitor": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: file-read, data-collection
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