Unified AI/LLM Security Scanner - Static Code Analysis + Live Model Testing

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Project links

Project description

AI Security CLI

A unified command-line tool for AI/LLM security scanning and testing. Combines static code analysis with live model testing to provide comprehensive security assessment for AI applications.

Website: aisentry.co

Benchmarks

Evaluated against a comprehensive OWASP LLM Top 10 testbed with 73 ground-truth vulnerabilities.

Metric	AI Security CLI	Semgrep	Bandit
Precision	69.6%	66.7%	51.5%
Recall	53.4%	8.2%	46.6%
F1 Score	60.5%	14.6%	48.9%

Per-Category Detection (Recall):

Category	Recall	Precision	F1
LLM07: Insecure Plugin	85.7%	85.7%	85.7%
LLM06: Sensitive Info	71.4%	55.6%	62.5%
LLM04: Model DoS	66.7%	100%	80.0%
LLM09: Overreliance	66.7%	100%	80.0%
LLM05: Supply Chain	60.0%	54.5%	57.1%
LLM01: Prompt Injection	50.0%	75.0%	60.0%
LLM10: Model Theft	42.9%	75.0%	54.5%
LLM03: Training Poisoning	40.0%	100%	57.1%
LLM08: Excessive Agency	33.3%	100%	50.0%
LLM02: Insecure Output	30.0%	42.9%	35.3%

AI Security CLI outperforms both Semgrep and Bandit on F1 score by detecting LLM-specific vulnerabilities that generic tools miss.

Features

Static Code Analysis: Scan Python codebases for OWASP LLM Top 10 vulnerabilities
Security Posture Audit: Auto-detect security controls and generate maturity scores across 10 categories (61 controls)
Remote Repository Scanning: Scan GitHub, GitLab, and Bitbucket repositories directly via URL
Interactive HTML Reports: Modern reports with tabbed interface, dark mode, severity filtering, and pagination
SARIF Output: CI/CD integration with GitHub Code Scanning, Azure DevOps, VS Code, and more
Configurable: YAML config files, environment variables, per-category thresholds, test file handling
4-Factor Confidence Scoring: Advanced confidence calculation for accurate vulnerability assessment

Live Model Testing

For live/runtime testing of LLM models (prompt injection, jailbreaks, etc.), we recommend Garak - a comprehensive LLM vulnerability scanner by NVIDIA.

# Install Garak
pip install garak

# Run probes against a model
garak --model_type openai --model_name gpt-4 --probes all

AI Security CLI focuses on static code analysis - finding vulnerabilities in your source code before deployment. Garak complements this by testing the runtime behavior of deployed models.

Installation

# Basic installation
pip install ai-security-cli

# With cloud provider support
pip install ai-security-cli[cloud]

# Development installation
pip install ai-security-cli[dev]

# Full installation with all features
pip install ai-security-cli[all]

Configuration

Config File (.ai-security.yaml)

Create a .ai-security.yaml file in your project root:

# Scan mode: recall (high sensitivity) or strict (higher thresholds)
mode: recall

# Deduplication: exact (merge duplicates) or off
dedup: exact

# Directories to exclude
exclude_dirs:
  - vendor
  - third_party
  - node_modules

# Test file handling
exclude_tests: false
demote_tests: true
test_confidence_penalty: 0.25

# Per-category confidence thresholds
thresholds:
  LLM01: 0.70
  LLM02: 0.70
  LLM05: 0.80
  LLM06: 0.75

# Global threshold (used if category not specified)
global_threshold: 0.70

Environment Variables

Variable	Description	Example
`AISEC_MODE`	Scan mode	`recall` or `strict`
`AISEC_DEDUP`	Deduplication	`exact` or `off`
`AISEC_EXCLUDE_DIRS`	Comma-separated dirs	`vendor,third_party`
`AISEC_THRESHOLD`	Global threshold	`0.70`
`AISEC_THRESHOLD_LLM01`	Per-category threshold	`0.80`

Precedence: CLI flags > Environment variables > .ai-security.yaml > Defaults

Quick Start

# Static code analysis (local)
ai-security-cli scan ./my_project

# Static code analysis (remote GitHub repository)
ai-security-cli scan https://github.com/langchain-ai/langchain

# Generate HTML report with Security Posture audit (default)
ai-security-cli scan ./my_project -o html -f security_report.html

# Security posture audit only
ai-security-cli audit ./my_project

# Live model testing
export OPENAI_API_KEY=sk-...
ai-security-cli test -p openai -m gpt-4 --mode quick

HTML Report Features

The HTML reports include a modern, interactive interface:

Tabbed Interface: Switch between Vulnerabilities and Security Posture views
Dark Mode: Toggle between light and dark themes (persists in browser)
Severity Filtering: Click severity buttons to filter by Critical, High, Medium, Low
Pagination: "Show More" button loads items in batches of 10
Combined Scoring: See both vulnerability score and security posture score
Hover Effects: Cards and items highlight on hover for better UX

Architecture

High-Level Overview

┌──────────────────────────────────────────────────────────────────────────────────────┐
│                                  AI SECURITY CLI                                      │
├──────────────────────────────────────────────────────────────────────────────────────┤
│                                                                                       │
│  ┌────────────────┐    ┌────────────────┐    ┌────────────────┐                      │
│  │  scan command  │    │ audit command  │    │  test command  │                      │
│  └───────┬────────┘    └───────┬────────┘    └───────┬────────┘                      │
│          │                     │                     │                                │
│          ▼                     ▼                     ▼                                │
│  ┌───────────────────┐ ┌───────────────────┐ ┌───────────────────┐                   │
│  │  STATIC ANALYSIS  │ │  SECURITY AUDIT   │ │   LIVE TESTING    │                   │
│  │                   │ │                   │ │                   │                   │
│  │ • AST Parser      │ │ • 61 Controls     │ │ • 7 LLM Providers │                   │
│  │ • 10 OWASP Detect │ │ • 10 Categories   │ │ • 11 Detectors    │                   │
│  │ • 7 Scorers       │ │ • Maturity Score  │ │ • 4-Factor Conf.  │                   │
│  └─────────┬─────────┘ └─────────┬─────────┘ └─────────┬─────────┘                   │
│            │                     │                     │                              │
│            └─────────────────────┼─────────────────────┘                              │
│                                  ▼                                                    │
│                    ┌──────────────────────────────┐                                  │
│                    │      REPORT GENERATION       │                                  │
│                    │  JSON | HTML | SARIF | Text  │                                  │
│                    │                              │                                  │
│                    │  HTML Features:              │                                  │
│                    │  • Tabbed Interface          │                                  │
│                    │  • Dark Mode Toggle          │                                  │
│                    │  • Severity Filtering        │                                  │
│                    │  • Pagination                │                                  │
│                    └──────────────────────────────┘                                  │
└──────────────────────────────────────────────────────────────────────────────────────┘

Static Analysis Flow

┌──────────────────────────────────────────────────────────────────────────────────┐
│                           STATIC ANALYSIS PIPELINE                                │
└──────────────────────────────────────────────────────────────────────────────────┘

  ┌─────────┐      ┌─────────────┐      ┌────────────────────────────────────────┐
  │ Python  │      │  AST Parser │      │         10 OWASP DETECTORS             │
  │  Code   │─────▶│  & Pattern  │─────▶│                                        │
  │ (.py)   │      │  Extractor  │      │  ┌──────────┐ ┌──────────┐ ┌────────┐ │
  └─────────┘      └─────────────┘      │  │  LLM01   │ │  LLM02   │ │ LLM03  │ │
                                        │  │  Prompt  │ │ Insecure │ │Training│ │
                                        │  │ Injection│ │  Output  │ │Poison  │ │
                                        │  └──────────┘ └──────────┘ └────────┘ │
                                        │  ┌──────────┐ ┌──────────┐ ┌────────┐ │
                                        │  │  LLM04   │ │  LLM05   │ │ LLM06  │ │
                                        │  │Model DoS │ │  Supply  │ │Secrets │ │
                                        │  │          │ │  Chain   │ │        │ │
                                        │  └──────────┘ └──────────┘ └────────┘ │
                                        │  ┌──────────┐ ┌──────────┐ ┌────────┐ │
                                        │  │  LLM07   │ │  LLM08   │ │ LLM09  │ │
                                        │  │ Insecure │ │Excessive │ │  Over  │ │
                                        │  │  Plugin  │ │ Agency   │ │reliance│ │
                                        │  └──────────┘ └──────────┘ └────────┘ │
                                        │  ┌──────────┐                         │
                                        │  │  LLM10   │                         │
                                        │  │  Model   │                         │
                                        │  │  Theft   │                         │
                                        │  └──────────┘                         │
                                        └───────────────────┬────────────────────┘
                                                            │
                                                            ▼
  ┌────────────────────────────────────────────────────────────────────────────────┐
  │                            7 SECURITY SCORERS                                   │
  │                                                                                 │
  │   ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐            │
  │   │  Prompt  │ │  Model   │ │   Data   │ │Hallucin- │ │ Ethical  │            │
  │   │ Security │ │ Security │ │ Privacy  │ │  ation   │ │    AI    │            │
  │   └──────────┘ └──────────┘ └──────────┘ └──────────┘ └──────────┘            │
  │   ┌──────────┐ ┌──────────┐                                                    │
  │   │Governance│ │  OWASP   │                                                    │
  │   │          │ │  Score   │                                                    │
  │   └──────────┘ └──────────┘                                                    │
  └───────────────────────────────────────────┬────────────────────────────────────┘
                                              │
                                              ▼
                            ┌─────────────────────────────────┐
                            │          SCAN RESULT            │
                            │  • Findings    • Category Scores│
                            │  • Overall Score  • Confidence  │
                            └─────────────────────────────────┘

Live Testing Flow

┌──────────────────────────────────────────────────────────────────────────────────┐
│                            LIVE TESTING PIPELINE                                  │
└──────────────────────────────────────────────────────────────────────────────────┘

┌────────────────────────────────────────────────────────────────────────────────┐
│                              7 LLM PROVIDERS                                    │
│  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌───────┐ ┌─────┐│
│  │ OpenAI  │ │Anthropic│ │ AWS     │ │ Google  │ │  Azure  │ │Ollama │ │Cust-││
│  │         │ │         │ │ Bedrock │ │ Vertex  │ │ OpenAI  │ │(local)│ │ om  ││
│  └────┬────┘ └────┬────┘ └────┬────┘ └────┬────┘ └────┬────┘ └───┬───┘ └──┬──┘│
└───────┴──────────┴──────────┴──────────┴──────────┴─────────┴────────┴──────┘
                                        │
                                        ▼
                          ┌──────────────────────────┐
                          │    BASELINE QUERIES      │
                          └────────────┬─────────────┘
                                       │
                                       ▼
┌────────────────────────────────────────────────────────────────────────────────┐
│                            11 LIVE DETECTORS                                    │
│  ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐│
│  │  Prompt  │ │Jailbreak │ │   Data   │ │ Halluc-  │ │   DoS    │ │   Bias   ││
│  │Injection │ │          │ │ Leakage  │ │ ination  │ │          │ │Detection ││
│  └──────────┘ └──────────┘ └──────────┘ └──────────┘ └──────────┘ └──────────┘│
│  ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐            │
│  │  Model   │ │Adversar- │ │  Output  │ │  Supply  │ │Behavioral│            │
│  │Extraction│ │   ial    │ │  Manip.  │ │  Chain   │ │ Anomaly  │            │
│  └──────────┘ └──────────┘ └──────────┘ └──────────┘ └──────────┘            │
└───────────────────────────────────────────┬────────────────────────────────────┘
                                            │
                                            ▼
┌────────────────────────────────────────────────────────────────────────────────┐
│                        4-FACTOR CONFIDENCE CALCULATION                          │
│                                                                                 │
│    Response Analysis (30%) + Detector Logic (35%) +                            │
│    Evidence Quality (25%) + Severity Factor (10%) = Confidence Score           │
└───────────────────────────────────────────┬────────────────────────────────────┘
                                            │
                                            ▼
                          ┌─────────────────────────────────┐
                          │          TEST RESULT            │
                          │  • Vulnerabilities  • Score     │
                          │  • Tests Passed   • Confidence  │
                          └─────────────────────────────────┘

Component Architecture

┌─────────────────────────────────────────────────────────────────────────────────┐
│                           ai_security package                                    │
├─────────────────────────────────────────────────────────────────────────────────┤
│                                                                                  │
│  ┌─────────────────────────────────────────────────────────────────────────┐    │
│  │                           CLI LAYER (cli.py)                             │    │
│  │    scan command ─────────────────────────── test command                 │    │
│  └─────────┬───────────────────────────────────────────┬───────────────────┘    │
│            │                                           │                         │
│            ▼                                           ▼                         │
│  ┌──────────────────────────┐            ┌──────────────────────────┐           │
│  │      scanner.py          │            │       tester.py          │           │
│  └────────────┬─────────────┘            └────────────┬─────────────┘           │
│               │                                       │                          │
│      ┌────────┴────────┐                    ┌─────────┴─────────┐               │
│      ▼                 ▼                    ▼                   ▼               │
│  ┌────────────┐  ┌────────────┐      ┌────────────┐    ┌────────────┐          │
│  │  STATIC    │  │  SCORERS   │      │   LIVE     │    │ PROVIDERS  │          │
│  │ DETECTORS  │  │            │      │ DETECTORS  │    │            │          │
│  │ LLM01-10   │  │ 7 scorers  │      │ 11 detects │    │ 7 providers│          │
│  └────────────┘  └────────────┘      └────────────┘    └────────────┘          │
│                                                                                  │
│  ┌─────────────────────────────────────────────────────────────────────────┐    │
│  │  REPORTERS: base | json | html | sarif                                   │    │
│  └─────────────────────────────────────────────────────────────────────────┘    │
│  ┌─────────────────────────────────────────────────────────────────────────┐    │
│  │  MODELS: finding.py | vulnerability.py | result.py                       │    │
│  └─────────────────────────────────────────────────────────────────────────┘    │
│  ┌─────────────────────────────────────────────────────────────────────────┐    │
│  │  UTILS: markov_chain | entropy | scoring | statistical                   │    │
│  └─────────────────────────────────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────────────────────────────────┘

CLI Commands

Static Code Analysis (`scan`)

Scan Python code for OWASP LLM Top 10 vulnerabilities. Supports local files/directories and remote Git repositories.

ai-security-cli scan <path> [OPTIONS]

Path Options:

Path Type	Example
Local file	`./app.py`
Local directory	`./my_project`
GitHub URL	`https://github.com/user/repo`
GitLab URL	`https://gitlab.com/user/repo`
Bitbucket URL	`https://bitbucket.org/user/repo`

Options:

Option	Description	Default
`-o, --output`	Output format: text, json, html, sarif	text
`-f, --output-file`	Write output to file	-
`-s, --severity`	Minimum severity: critical, high, medium, low, info	info
`-c, --confidence`	Minimum confidence threshold (0.0-1.0)	0.7
`--category`	Filter by OWASP category (LLM01-LLM10)	all
`--audit/--no-audit`	Include security posture audit in HTML reports	true
`--config`	Path to .ai-security.yaml config file	auto-detect
`--mode`	Scan mode: recall (sensitive) or strict (precise)	recall
`--dedup`	Deduplication: exact (merge) or off	exact
`--exclude-dir`	Directories to exclude (repeatable)	-
`--exclude-tests`	Skip test files entirely	false
`--demote-tests`	Reduce confidence for test file findings	true
`-v, --verbose`	Enable verbose output	false

Examples:

# Scan a local project directory
ai-security-cli scan ./my_llm_app

# Scan with JSON output
ai-security-cli scan ./app.py -o json -f results.json

# Scan for high severity issues only
ai-security-cli scan ./project -s high

# Scan specific OWASP categories
ai-security-cli scan ./project --category LLM01 --category LLM02

# Generate HTML report
ai-security-cli scan ./project -o html -f security_report.html

# Scan a GitHub repository directly
ai-security-cli scan https://github.com/langchain-ai/langchain

# Generate HTML without security posture audit
ai-security-cli scan ./project -o html --no-audit -f vuln-only.html

Security Posture Audit (`audit`)

Evaluate security controls and maturity level of your codebase. Detects 61 security controls across 10 categories.

ai-security-cli audit <path> [OPTIONS]

Options:

Option	Description	Default
`-o, --output`	Output format: text, json, html	text
`-f, --output-file`	Write output to file	-
`-v, --verbose`	Enable verbose output	false

Security Control Categories:

Category	Controls	Description
Prompt Security	8	Input validation, sanitization, injection prevention, red teaming
Model Security	8	Rate limiting, access controls, model protection, differential privacy
Data Privacy	8	PII detection, encryption, data anonymization, GDPR compliance
OWASP LLM Top 10	10	Coverage of OWASP LLM security controls
Blue Team Operations	7	Logging, monitoring, alerting, drift detection
Governance	5	Compliance, documentation, audit trails
Supply Chain	3	Dependency scanning, model provenance, integrity verification
Hallucination Mitigation	5	RAG implementation, confidence scoring, fact checking
Ethical AI & Bias	4	Fairness metrics, explainability, bias testing, model cards
Incident Response	3	Monitoring integration, audit logging, rollback capability

Maturity Levels:

Level	Score	Description
Initial	0-20	No formal security controls
Developing	21-40	Basic controls being implemented
Defined	41-60	Documented security processes
Managed	61-80	Measured and controlled security
Optimizing	81-100	Continuous security improvement

Examples:

# Audit a local project
ai-security-cli audit ./my_project

# Generate HTML audit report
ai-security-cli audit ./project -o html -f audit-report.html

# Audit a GitHub repository
ai-security-cli audit https://github.com/user/repo -o json

Live Model Testing (`test`)

Test live LLM models for security vulnerabilities.

ai-security-cli test [OPTIONS]

Options:

Option	Description	Default
`-p, --provider`	LLM provider (required)	-
`-m, --model`	Model name (required)	-
`-e, --endpoint`	Custom endpoint URL	-
`-t, --tests`	Specific tests to run	all
`--mode`	Testing depth: quick, standard, comprehensive	standard
`-o, --output`	Output format: text, json, html, sarif	text
`-f, --output-file`	Write output to file	-
`--timeout`	Timeout per test in seconds	30
`-v, --verbose`	Enable verbose output	false

Supported Providers:

Provider	Environment Variables
`openai`	`OPENAI_API_KEY`
`anthropic`	`ANTHROPIC_API_KEY`
`bedrock`	`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`
`vertex`	`GOOGLE_APPLICATION_CREDENTIALS`
`azure`	`AZURE_OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`
`ollama`	None (local)
`custom`	`CUSTOM_API_KEY` (optional)

Examples:

# Quick test with OpenAI
export OPENAI_API_KEY=sk-...
ai-security-cli test -p openai -m gpt-4 --mode quick

# Comprehensive test with Anthropic
export ANTHROPIC_API_KEY=...
ai-security-cli test -p anthropic -m claude-3-opus --mode comprehensive

# Test specific vulnerabilities
ai-security-cli test -p openai -m gpt-4 -t prompt-injection -t jailbreak

# Test with Ollama (local)
ai-security-cli test -p ollama -m llama2 --mode standard

OWASP LLM Top 10 Coverage

Static Analysis Detectors

ID	Vulnerability	Description
LLM01	Prompt Injection	Detects unsanitized user input in prompts
LLM02	Insecure Output Handling	Identifies unvalidated LLM output
LLM03	Training Data Poisoning	Finds unsafe data loading
LLM04	Model Denial of Service	Detects missing rate limiting
LLM05	Supply Chain Vulnerabilities	Identifies unsafe model loading
LLM06	Sensitive Information Disclosure	Finds hardcoded secrets
LLM07	Insecure Plugin Design	Detects unsafe plugin loading
LLM08	Excessive Agency	Identifies autonomous actions
LLM09	Overreliance	Finds missing output validation
LLM10	Model Theft	Detects exposed model artifacts

Live Testing Detectors

ID	Detector	Description
PI	Prompt Injection	Tests for injection vulnerabilities
JB	Jailbreak	Tests for instruction bypass attacks
DL	Data Leakage	Tests for PII exposure
HAL	Hallucination	Tests for factual accuracy
DOS	Denial of Service	Tests for resource exhaustion
BIAS	Bias Detection	Tests for demographic bias
ME	Model Extraction	Tests for architecture disclosure
ADV	Adversarial Inputs	Tests for encoding attacks
OM	Output Manipulation	Tests for response injection
SC	Supply Chain	Tests for unsafe code generation
BA	Behavioral Anomaly	Tests for unexpected behavior

Output Formats

Text: Human-readable terminal output
JSON: Machine-readable format for CI/CD
HTML: Interactive reports with filtering
SARIF: GitHub Code Scanning, Azure DevOps, VS Code integration

Integration

GitHub Actions

name: AI Security Scan
on: [push, pull_request]
jobs:
  security-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-python@v4
        with:
          python-version: '3.11'
      - run: pip install ai-security-cli
      - run: ai-security-cli scan . -o sarif -f results.sarif
      - uses: github/codeql-action/upload-sarif@v2
        with:
          sarif_file: results.sarif

Pre-commit Hook

repos:
  - repo: local
    hooks:
      - id: ai-security-scan
        name: AI Security Scan
        entry: ai-security-cli scan
        language: system
        types: [python]
        args: ['-s', 'high']

Development

git clone https://github.com/deosha/ai-security-cli.git
cd ai-security-cli
pip install -e ".[dev]"
pytest tests/ -v --cov=ai_security

License

MIT License - see LICENSE for details.

Project details

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Jan 10, 2026

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ai-security-cli 1.0.0b4

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AI Security CLI

Benchmarks

Features

Live Model Testing

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Configuration

Config File (.ai-security.yaml)

Environment Variables

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HTML Report Features

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Static Analysis Flow

Live Testing Flow

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CLI Commands

Static Code Analysis (scan)

Security Posture Audit (audit)

Live Model Testing (test)

OWASP LLM Top 10 Coverage

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Live Testing Detectors

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Static Code Analysis (`scan`)

Security Posture Audit (`audit`)

Live Model Testing (`test`)