Why This Review Exists

Most "best security tools" lists are affiliate roundups. This one is not. We are a security company with a specific product (AI proxy mutation) and a specific gap we fill. Every tool listed here does something we do not do. Some of them you should use alongside Pretense. All of them are worth knowing.

The organizing principle: every tool here operates on static code or git history. Pretense operates at the AI API boundary, at the moment code is transmitted to an external LLM. They are complementary, not competing.

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Gitleaks

**What it does:** Scans git repositories (including full history) for secrets, credentials, and tokens. Supports 150+ secret patterns out of the box.

**Install time:** Under 5 minutes.

bash

# Install via Homebrew

# Scan current repo gitleaks detect --source=. --verbose

# Scan full git history gitleaks detect --source=. --log-opts="--all"

# As a pre-commit hook gitleaks protect --staged ```

**False positive rate:** Low to medium. Gitleaks is conservative by default and has good entropy-based filtering. The main false positive source is test fixture files that contain mock credentials.

**What it catches:** AWS keys, GitHub tokens, Stripe secrets, Slack webhooks, JWT secrets, private keys, and over 150 other patterns. Catches secrets that were committed and deleted (via history scan).

**What it misses:** It does not know about secrets that never touched git: environment variables in developer shells, secrets pasted into AI prompts, or credentials in developer memory that are never written to files.

**Verdict:** Install on every project. Add the pre-commit hook. Run the history scan on any repo that is more than six months old. You will likely find something.

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Semgrep

**What it does:** Static analysis security testing (SAST) with a rule-based engine. Matches code patterns across the entire project, not just secrets. Catches SQL injection patterns, XSS vectors, insecure deserialization, and hundreds of other vulnerability classes.

**Install time:** Under 10 minutes including a first scan.

bash

# Install

# Run the security audit ruleset semgrep --config=p/security-audit .

# Run for TypeScript/JavaScript semgrep --config=p/typescript .

# Run for Python semgrep --config=p/python . ```

**False positive rate:** Medium. Semgrep rules vary widely in quality. The maintained rulesets (p/security-audit, p/owasp-top-ten) are well-tuned. Custom rules can produce significant noise.

**What it catches:** Known vulnerable patterns: unvalidated input passed to SQL queries, secrets hardcoded in source files, use of deprecated cryptographic functions, prototype pollution patterns, path traversal vectors.

**What it misses:** Business logic vulnerabilities, authorization flaws (is this user allowed to access this resource?), and anything that requires understanding the application's data flow at runtime.

**Verdict:** Run Semgrep in CI on every pull request. The security-audit ruleset is worth the noise for the findings it surfaces. Do not write custom rules until you have exhausted the maintained ones.

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Trivy

**What it does:** Container and dependency vulnerability scanner. Scans Docker images, Kubernetes manifests, Terraform configurations, and package lock files for known CVEs.

**Install time:** Under 5 minutes.

bash

# Install via Homebrew

# Scan a Docker image trivy image node:20-alpine

# Scan filesystem (catches package.json, requirements.txt, etc.) trivy fs .

# Scan Kubernetes manifest trivy k8s --report summary cluster

# Scan Terraform config trivy config ./terraform/ ```

**False positive rate:** Low. Trivy matches against the NVD (National Vulnerability Database) and GitHub Advisory Database, so findings are verified CVEs with known severity ratings.

**What it catches:** Outdated packages with known vulnerabilities, misconfigured container images, insecure Kubernetes configurations, exposed ports, and privilege escalation paths in infrastructure-as-code.

**What it misses:** Zero-day vulnerabilities (by definition), configuration issues that are organization-specific, and anything requiring runtime analysis.

**Verdict:** Run Trivy in CI on every Docker build and on every pull request that touches infrastructure configuration. The container scanning is particularly valuable for catching base image vulnerabilities that developers do not think about.

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detect-secrets

**What it does:** Pre-commit hook and CI scanner for secrets. Maintains a baseline file of acceptable false positives, making it more manageable than tools that produce noise in well-established codebases.

**Install time:** Under 5 minutes.

bash

# Install

# Create a baseline (scan existing codebase, mark known false positives) detect-secrets scan > .secrets.baseline

# Audit the baseline to mark false positives detect-secrets audit .secrets.baseline

# Run as pre-commit hook (prevents new secrets from being committed) detect-secrets scan --baseline .secrets.baseline ```

**False positive rate:** Low after baseline is established. The baseline approach is the key differentiator: you audit the initial findings once, mark the test fixtures and mock credentials as false positives, and subsequent scans only flag new findings.

**What it catches:** Similar pattern to Gitleaks: API keys, tokens, credentials, private keys, password assignments. The baseline approach makes it more practical for existing projects.

**What it misses:** Same as Gitleaks: runtime secrets, secrets in AI prompts, environment variables that never touch files.

**Verdict:** Use detect-secrets if you have an existing codebase with pre-existing false positives that make zero-tolerance tools impractical. The baseline model makes adoption easier for mature projects.

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truffleHog

**What it does:** Deep git history scanner with entropy analysis and verified scanning. Sends potential secrets to the issuing service to verify if they are active credentials (with your permission).

**Install time:** Under 5 minutes.

bash

# Install via Homebrew

# Scan git history trufflehog git https://github.com/your-org/your-repo

# Scan local filesystem trufflehog filesystem /path/to/project

# Scan Docker image trufflehog docker --image=your-image:tag

# Only verified secrets (reduces noise significantly) trufflehog git --only-verified https://github.com/your-org/your-repo ```

**False positive rate:** Medium without `--only-verified`, low with it. The verified scanning feature is the key differentiator: truffleHog can confirm whether a found API key is still active by making a test request to the relevant service. This dramatically reduces noise.

**What it catches:** Everything Gitleaks catches, plus historical commits that were squashed or rebased out of the main branch, and credentials in submodules.

**What it misses:** Same class of blind spots as all git-based scanners: runtime secrets and AI prompt exfiltration.

**Verdict:** Run truffleHog with `--only-verified` on repositories before open-sourcing them or before security audits. The verified scanning is worth the slightly higher runtime compared to Gitleaks.

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Pretense

**What it does:** Intercepts outbound LLM API requests and mutates proprietary code identifiers before they leave your network. Operates at the AI API boundary.

**Install time:** Under 5 minutes.

bash

# Install and start
curl -fsSL https://pretense.ai/install.sh | sh
pretense init

# Route Claude Code through Pretense export ANTHROPIC_BASE_URL=http://localhost:9339 ```

**False positive rate:** Near zero. Mutation does not remove code, it replaces identifier names with deterministic synthetics. The LLM receives structurally equivalent code and produces useful output.

**What it catches:** Proprietary function names, class names, variable names, and API surface area transmitted to LLM providers. Also catches secrets matching 30+ patterns and blocks them before transmission.

**What it misses:** Freeform natural language prompts referencing proprietary names, commit messages, file paths, and anything outside code blocks.

**Verdict:** Complementary to all tools above. The other tools secure your codebase and dependencies. Pretense secures the moment your code is transmitted to an AI API. They address different attack surfaces.

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The Layered Stack

A complete AI-era security stack looks like this:

Layer	Tool	What It Secures
Pre-commit	detect-secrets or Gitleaks	Secrets never reach git
CI/CD	Semgrep + Trivy	Known vuln patterns + container CVEs
Git history	truffleHog --only-verified	Historical credential exposure
AI API boundary	Pretense	Identifiers and secrets in LLM transit

None of these tools overlap significantly. All four are worth running. The total overhead for a 10-person team is approximately 2 hours of initial setup and under 30 minutes per week of CI/CD review time.

[Get started with Pretense at pretense.ai/docs](/docs)