["Analyze code changes in pull requests to catch vulnerabilities early","Always check dependencies for CVEs via OSV.dev integration","Produce structured JSON output for automated pipelines","Flag LLM-specific risks (prompt injection, unsafe output handling)","Report precision and recall benchmarks when assessing coverage"]
error_handling
graceful
streaming
supported
verified
false
lastVerifiedAt
"2026-02-19T05:29:09.098Z"
source
builtin
trust_score
100
provenance_sha
bef705454e10ccb7
Gemini CLI Security Skill
AI-powered security analysis skill adapted from the Gemini CLI Security Extension (github.com/gemini-cli-extensions/security). Provides vulnerability detection across code and dependencies with 90% precision and 93% recall on TypeScript/JavaScript CVE datasets.
- Code vulnerability analysis (/security:analyze pattern)
- OSV.dev dependency scanning (/security:scan-deps pattern)
- Hardcoded credentials and secrets detection
- Injection attack detection (XSS, SQL, command, SSRF, template)
- Weak cryptography and insecure deserialization detection
- Authentication and session management flaw detection
- LLM-specific risks: prompt injection, unsafe output handling
- JSON output formatting for CI/CD pipeline integration
- GitHub Actions integration patterns for automated PR analysis
Overview
This skill adapts the Gemini CLI Security Extension's analysis methodology for the agent-studio framework. The original extension uses two MCP server patterns — a security analysis server and an OSV-Scanner integration — to provide dual-vector coverage. This skill implements equivalent analysis using native Claude Code tools (WebFetch for OSV.dev API, Grep/Bash for static analysis patterns).
Before merging pull requests to detect introduced vulnerabilities
During security reviews of new code changes
For dependency auditing against known CVE databases
For LLM-integrated applications requiring prompt injection defense review
As part of CI/CD pipeline security gates
Iron Law
NO PRODUCTION CODE WITHOUT SECURITY ANALYSIS FOR AUTH/SECRETS/EXTERNAL-INPUT HANDLERS
All code paths handling authentication, hardcoded values, external input, or AI model outputs MUST be analyzed before production deployment.
Vulnerability Coverage
Category 1: Secrets Management
Pattern
Detection Method
Hardcoded API keys
Grep for key patterns + entropy analysis
Hardcoded passwords
Credential keyword detection
Private keys in source
PEM block / base64 key detection
Encryption keys
Symmetric key constant patterns
Category 2: Injection Attacks
Attack Type
Examples
SQL injection
String concatenation in queries
XSS
Unescaped user content in HTML/JS output
Command injection
Shell exec with user-controlled args
SSRF
User-controlled URLs in server requests
Template injection
Unsanitized user input in template engines
Category 3: Authentication Flaws
Flaw
Detection
Session bypass
Missing auth middleware
Weak tokens
Predictable token generation
Insecure password reset
Token-less or email-only resets
Missing MFA enforcement
Auth flows without 2FA checks
Category 4: Data Handling
Issue
Detection
Weak cryptography
MD5/SHA1 for secrets; DES/RC4 usage
Sensitive data in logs
PII/credential patterns in log statements
PII violations
Unencrypted PII storage or transmission
Insecure deserialization
Unsafe pickle/eval/deserialize calls
Category 5: LLM Safety (Novel)
Risk
Detection
Prompt injection
User content injected into LLM prompts without sanitization
Unsafe output handling
LLM output used in exec/eval/shell without validation
Insecure tool integration
Tool calls with unchecked LLM-provided parameters
Usage
Invocation
// From an agentSkill({ skill: 'gemini-cli-security' });
// With arguments via Bash integrationSkill({ skill: 'gemini-cli-security', args: 'src/ --scan-deps' });
Workflow Execution
# Analyze code in a directory
node .claude/skills/gemini-cli-security/scripts/main.cjs --target src/
# Scan dependencies for CVEs
node .claude/skills/gemini-cli-security/scripts/main.cjs --scan-deps
# JSON output for CI integration
node .claude/skills/gemini-cli-security/scripts/main.cjs --target . --json
# Scoped analysis with natural language
node .claude/skills/gemini-cli-security/scripts/main.cjs --target src/auth/ --scope "focus on token handling and session management"
Output Format
Default output (markdown report):
## Security Analysis Report### CRITICAL- [AUTH-001] Hardcoded API key found in src/config.ts:42
Pattern: `const API_KEY = "sk-..."`
Remediation: Move to environment variable
### HIGH- [INJ-002] SQL injection risk in src/db/users.ts:87
Pattern: String concatenation in query builder
Remediation: Use parameterized queries
### Dependencies- lodash@4.17.15 → CVE-2021-23337 (HIGH) - Prototype pollution
Fix: Upgrade to lodash@4.17.21+
JSON output (--json flag):
{"findings":[{"id":"AUTH-001","severity":"CRITICAL","category":"secrets","file":"src/config.ts","line":42,"description":"Hardcoded API key","remediation":"Move to environment variable"}],"dependencies":[{"package":"lodash","version":"4.17.15","cve":"CVE-2021-23337","severity":"HIGH","fix":"4.17.21"}],"summary":{"critical":1,"high":2,"medium":3,"low":0,"precision":0.9,"recall":0.93}}
OSV.dev Dependency Scanning
The skill integrates with the OSV.dev API (no authentication required) to check dependencies:
// OSV.dev batch query endpointWebFetch({
url: 'https://api.osv.dev/v1/querybatch',
prompt: 'Extract vulnerability IDs, severity, and affected versions for these packages',
});
Why native tools over MCP servers:
The original extension uses two MCP servers (security analysis server + OSV-Scanner binary). This skill uses native Claude Code tools instead:
WebFetch replaces OSV-Scanner for dependency CVE lookups (OSV.dev has a public REST API)
Grep/Bash replace the security analysis server for pattern-based detection
This approach works immediately without binary installation or session restart
Deviation from source: The original uses Gemini AI for code analysis; this skill uses the pattern-based detection methodology documented in the extension's benchmarking. The AI analysis component can be provided by the invoking agent (security-architect) rather than an embedded AI call.
Assigned Agents
Agent
Role
security-architect
Primary: comprehensive security audits
developer
Supporting: pre-commit security checks
code-reviewer
Supporting: PR review security layer
Memory Protocol (MANDATORY)
Before starting:
Read .claude/context/memory/learnings.md
After completing:
New vulnerability pattern found -> .claude/context/memory/learnings.md
Issue with scanning -> .claude/context/memory/issues.md
Decision about scope -> .claude/context/memory/decisions.md
ASSUME INTERRUPTION: If it's not in memory, it didn't happen.
