plugin-security

Assess prompt injection risks in LLM extensions by applying a threat modeling framework that identifies hijacking vectors targeting model behavior. This process delivers an attack taxonomy and practical heuristics to guide secure evaluation before enabling plugins.

Hook scripts in Claude Code plugins pose security threats by abusing privileged permissions to execute unauthorized actions or escalate access. Threat modeling detects this hook-script abuse alongside data exfiltration attempts to prevent compromised host environments.

Build a threat model for LLM extensions by reviewing plugin code for permission escalation, hook-script abuse, and prompt injection vulnerabilities. This generates a risk assessment complete with an attack taxonomy and guardrails for decision-making.

You can detect data exfiltration vulnerabilities in LLM skill packs by evaluating them against security heuristics designed to identify unauthorized data transmission. The assessment framework flags overly broad permissions that risk user data.

Prevent permission escalation in AI agents by applying practical heuristics and guardrails that restrict overly broad access rights. The threat modeling approach identifies privilege escalation paths before extensions are enabled.

Perform a security review of LLM extensions before enabling any plugin to ensure prompt injection and hook-script abuse vulnerabilities are identified. Early threat modeling prevents risk to model behavior and user data.