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Security audit

Agent Architecture Analysis

Security checks for vulnerabilities and agentic risk

Overview

This skill is a read-only architecture audit guide that asks the agent to inspect a user-provided codebase and produce evidence-based recommendations.

Install this if you want an agent to perform a structured architecture review of an LLM or agentic codebase. Expect it to read source files under the codebase path you provide and run search commands for evidence; review any generated recommendations before applying code changes.

A.I.G

Vulnerability Patterns
  • 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
  • Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs

SkillSpector

Vulnerability Patterns
  • Output HandlingUnvalidated Output Injection, Cross-Context Output, Unbounded Output
  • 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 (2)

Unvalidated Output Injection

High
Category
Output Handling
Confidence
80% confidence
Finding

Model output is used without validation or sanitization. Unvalidated output injected into downstream contexts (SQL, shell, HTML) enables injection attacks and arbitrary code execution.

Content

Scanner excerpt · references/factors.md (reported line 141)May include surrounding context.

md
| **Weak** | Tools return arbitrary dicts, no validation layer |

**Anti-patterns:**
- Tool handlers that directly execute LLM output
- `eval()` or `exec()` on LLM-generated code
- No separation between decision (LLM) and execution (code)
- Magic method dispatch based on string matching

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
89% confidence
Finding

This manifest description starts with several specific trigger phrases, but then broadens activation to 'also applies when comparing frameworks or planning agent improvements.' Those conditions are common, high-level activities that could overlap with many general architecture or planning requests, making invocation scope less precise.

Content

No source excerpt is available for this finding.