From soundcheck
Detects LLM tool definitions missing parameter validation, preventing prompt injection escalation to file/shell/network access. Use when writing tool schemas or plugin handlers.
How this skill is triggered — by the user, by Claude, or both
Slash command
/soundcheck:insecure-plugin-designThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Prevents LLM tools and plugins from being abused via malicious or malformed inputs
Prevents LLM tools and plugins from being abused via malicious or malformed inputs driven by prompt injection or jailbreaks. Unvalidated tool parameters let an attacker escalate from a chat window to arbitrary file access, command execution, or data exfiltration.
enum, maxLength, pattern, or additionalProperties: false constraintsaction parameter instead of separate handlersFlag the vulnerable code and explain the risk. Translate the principles below to the audited file's language, tool-definition schema, and authorization framework — use that stack's documented validation and access-control APIs.
For each finding, establish these properties:
maxLength, pattern, enum, additionalProperties: false), a typed enum
or sealed class, runtime validation at the top of the handler, or an allowlist
lookup — the goal is that a malformed value never reaches the handler body.action parameter. Narrow tools are easier to audit and harder to weaponize.Confirm the following properties hold (language-agnostic):
maxLength/pattern/enum/additionalProperties: false), a typed enum or sealed class in the method signature, runtime regex/length validation at the top of the handler, or an allowlist lookup against a static set — before the value is usedaction parameternpx claudepluginhub thejefflarson/soundcheck --plugin soundcheckDefines security patterns for LLM tool and plugin interfaces: input validation, minimal permissions, human confirmation, and sandboxed execution to prevent prompt injection attacks.
Detects direct and indirect prompt injection in LLM applications. Flags user input or retrieved documents that could hijack model instructions, and enforces trust-tier separation, input screening, and output validation.
Audit applications for AI prompt injection, agent security, and LLM permission boundary vulnerabilities. Use when securing AI features or agents.