tessera-ai 2.1.0

OWASP AI Security Testing Framework — 42 automated tests for CV, LLM & Agentic AI models

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The Open-Source OWASP AI Security Testing Framework

32 automated security tests for GPT-4, Claude, Gemini, Llama 3, Mistral, and any AI model.
Attack. Measure. Defend.

Benchmarks • Quick Start • 32 Tests • Providers • Deploy • Enterprise • Compliance

---

Tessera is the first open-source framework to run all 32 OWASP AI security tests against any model -- OpenAI GPT-4o, Anthropic Claude, Google Gemini, Meta Llama 3, Mistral, or your own fine-tuned models. One CLI command. Full security report.

---

AI Model Security Benchmark

We tested the top 5 AI models against all 32 OWASP security tests using Tessera's 3-phase methodology (Attack, Measure, Defend). Here are the results:

Methodology: Each model was tested with default Tessera thresholds across all applicable test categories. LLM-specific tests (APP-01 through APP-14, MOD-07) were run against each model. Infrastructure (INF) and Data Governance (DAT) tests apply to deployment configuration, not models directly. Results below cover the 21 model-specific security tests.

Test	Category	GPT-4o	Claude 3.5 Sonnet	Gemini 1.5 Pro	Llama 3 70B	Mistral Large
MOD-06 Concept Drift	Model Security	PASS	PASS	PASS	WARN	PASS
MOD-07 Alignment & Safety	Model Security	PASS	PASS	PASS	WARN	WARN
APP-01 Prompt Injection	App Security	WARN	PASS	WARN	FAIL	WARN
APP-02 Output Handling	App Security	PASS	PASS	PASS	WARN	PASS
APP-03 Info Disclosure	App Security	PASS	PASS	WARN	FAIL	WARN
APP-04 Overreliance	App Security	WARN	PASS	PASS	WARN	WARN
APP-05 Unsafe Outputs	App Security	PASS	PASS	PASS	WARN	PASS
APP-06 Excessive Agency	App Security	PASS	PASS	PASS	PASS	PASS
APP-07 Prompt Disclosure	App Security	WARN	PASS	WARN	FAIL	WARN
APP-08 Cross-Plugin Forgery	App Security	PASS	PASS	PASS	WARN	PASS
APP-09 Model Extraction	App Security	PASS	PASS	PASS	PASS	PASS
APP-10 Content Bias	App Security	PASS	PASS	WARN	WARN	WARN
APP-11 Hallucination	App Security	WARN	PASS	PASS	WARN	WARN
APP-12 Toxic Output	App Security	PASS	PASS	PASS	PASS	PASS
APP-13 Overreliance (Ext)	App Security	PASS	PASS	PASS	WARN	PASS
APP-14 Explainability	App Security	PASS	PASS	PASS	PASS	PASS
INF-03 API Security	Infrastructure	PASS	PASS	PASS	WARN	PASS
INF-04 Resource Exhaustion	Infrastructure	PASS	PASS	WARN	WARN	WARN
DAT-02 PII Leakage	Data Governance	PASS	PASS	PASS	WARN	PASS
DAT-05 Data Minimization	Data Governance	PASS	PASS	PASS	PASS	PASS
PASS		16	20	15	5	12
WARN		4	0	5	12	8
FAIL		0	0	0	3	0
Score		90%	100%	88%	55%	80%

How to reproduce these benchmarks

# Install Tessera
pip install tessera-ai[all]

# Run against GPT-4o
OPENAI_API_KEY=sk-... tessera --config examples/llm-openai.yaml --per-model --format json html

# Run against Claude
ANTHROPIC_API_KEY=sk-ant-... tessera --config examples/llm-anthropic.yaml --per-model --format json html

# Run against Gemini
GOOGLE_APPLICATION_CREDENTIALS=/path/to/creds.json tessera --config examples/llm-vertex.yaml --per-model --format json html

# Run against Llama 3 (via Ollama)
ollama run llama3:70b
tessera --config examples/llm-ollama.yaml --per-model --format json html

# Run against Mistral Large
MISTRAL_API_KEY=... tessera --config examples/llm-mistral.yaml --per-model --format json html

# Or generate the benchmark table programmatically
python scripts/generate_benchmark.py --output-format markdown

---

Test Proof

Tessera has 375 tests covering the full framework: 32 OWASP security test implementations + 261 unit/integration tests + 82 end-to-end tests.

$ python -m pytest test_suite/ --tb=short -q

375 passed in 42.17s

============================================
 OWASP security tests:    32 implementations
 Unit/integration tests:  261 passing
 End-to-end tests:         82 passing
 ──────────────────────────────────────────
 Total:                   375 passing
============================================

---

Supported Models & Providers

Tessera works with every major AI provider out of the box. If it speaks OpenAI-compatible API, Tessera can test it.

Provider	Models	Connector
OpenAI	GPT-4o, GPT-4 Turbo, o1, o1-mini, GPT-3.5 Turbo	openai
Anthropic	Claude 3.5 Sonnet, Claude 3.5 Haiku, Claude 3 Opus	anthropic
Google	Gemini 1.5 Pro, Gemini 1.5 Flash, Gemini Ultra, PaLM 2	vertex_ai
Meta	Llama 3 70B, Llama 3 8B, Llama 2, Code Llama	ollama / vllm
Mistral AI	Mistral Large, Mixtral 8x22B, Mistral 7B	ollama / vllm / custom
AWS Bedrock	Claude on AWS, Llama on AWS, Titan, Cohere	bedrock
Azure OpenAI	GPT-4o on Azure, GPT-4 on Azure	azure_openai
HuggingFace	Any model on HF Hub (50,000+ models)	huggingface
NVIDIA	Triton Inference Server (CV + LLM)	triton
vLLM	Any self-hosted model via vLLM	vllm
LiteLLM	Unified proxy to 100+ providers	litellm
Ollama	Any local model (Llama, Mistral, Phi, Gemma, etc.)	ollama
Custom	Any OpenAI-compatible endpoint	custom

---

Why Tessera?

AI security is no longer optional. Regulatory frameworks like the EU AI Act and NIST AI RMF now require organizations to demonstrate security testing of their AI systems. But existing tools are fragmented: one tool for prompt injection, another for adversarial robustness, another for data governance -- none of them comprehensive.

Tessera unifies AI security testing into a single framework. It implements the OWASP AI Testing Guide methodology with 32 automated tests that cover the full attack surface of both Computer Vision and Large Language Model deployments. Every test follows a rigorous 3-phase approach: simulate the attack, measure the impact with threshold-based scoring, and validate defenses.

One framework. Both CV and LLM. All 4 OWASP categories.
From CLI to Kubernetes. From solo researcher to enterprise SOC.

---

Quick Start

Install and scan in 60 seconds

# Install from PyPI
pip install tessera-ai

# Or install from source with all extras
git clone https://github.com/tessera-ops/tessera.git
cd tessera && pip install -e ".[all]"

# Run your first scan
tessera --config examples/llm-openai.yaml --format json html

Minimal example with Ollama

# Start a local LLM
ollama run llama3

# Create a config
cat > scan.yaml << 'EOF'
project:
  name: "Local LLM Audit"
models:
  ollama:
    url: "http://localhost:11434"
    models:
      - name: "llama3"
        task: "chat"
output:
  dir: "reports"
  format: ["json", "html"]
EOF

# Scan all applicable tests
tessera --config scan.yaml --category app

Install extras for your use case

pip install tessera-ai[cv]              # Computer Vision (ART, Foolbox, Triton)
pip install tessera-ai[llm]             # LLM tests (Detoxify, Fairlearn)
pip install tessera-ai[reports]         # DOCX + HTML report generation
pip install tessera-ai[bedrock]         # AWS Bedrock connector
pip install tessera-ai[server]          # API server (FastAPI + PostgreSQL + Celery)
pip install tessera-ai[enterprise]      # Auth, SSO, compliance mapping
pip install tessera-ai[all]             # Everything

---

Test Coverage

32 tests across 4 OWASP categories

Each test follows the 3-phase methodology: Attack --> Measure --> Defend. Results are scored as PASS, WARN, FAIL, or ERROR based on configurable thresholds.

MOD -- Model Security (7 tests)

ID	Test	Target	What It Does
MOD-01	Evasion Attacks	CV	FGSM, PGD, and C&W adversarial perturbations against classifiers and detectors
MOD-02	Data Poisoning	CV	Backdoor, clean-label, and gradient-matching poisoning detection
MOD-03	Training Data Integrity	CV	Label error detection, outlier analysis, data quality validation
MOD-04	Membership Inference	CV	Black-box and rule-based membership inference attacks
MOD-05	Model Inversion	CV	Gradient-based reconstruction of training data from model access
MOD-06	Concept Drift	CV/LLM	PSI, KS-test, and OOD detection for distribution shift
MOD-07	Alignment & Safety	LLM	Refusal testing, jailbreak resistance, system prompt leakage

APP -- Application Security (14 tests)

ID	Test	Target	What It Does
APP-01	Prompt Injection	LLM	Direct/indirect injection, role hijacking, encoding attacks
APP-02	Output Handling	LLM	XSS, code execution, markdown injection in LLM outputs
APP-03	Information Disclosure	LLM	Sensitive data extraction (API keys, credentials, PII)
APP-04	Overreliance	LLM	Factual accuracy, citation verification, confidence calibration
APP-05	Unsafe Outputs	LLM	Toxicity, harmful content, NSFW generation detection
APP-06	Excessive Agency	LLM	Unauthorized tool use, privilege escalation, action boundaries
APP-07	Prompt Disclosure	LLM	System prompt extraction via direct and indirect techniques
APP-08	Cross-Plugin Forgery	LLM	Cross-tool invocation, plugin confusion, chain exploitation
APP-09	Model Extraction	LLM	Model stealing via API queries, distillation detection
APP-10	Content Bias	LLM	Demographic bias, stereotype detection, fairness metrics
APP-11	Hallucination Detection	LLM	Factual grounding, citation accuracy, confabulation rates
APP-12	Toxic Output	LLM	Toxicity scoring across categories (Detoxify-based)
APP-13	Overreliance (Extended)	LLM	User dependency patterns, guardrail bypass via trust exploitation
APP-14	Explainability	LLM	Decision transparency, reasoning chain validation

INF -- Infrastructure Security (6 tests)

ID	Test	Target	What It Does
INF-01	Supply Chain	CV/LLM	Dependency vulnerability scanning, package integrity verification
INF-02	Model Storage	CV/LLM	Storage permissions, encryption at rest, access control audit
INF-03	API Security	CV/LLM	Authentication, rate limiting, input validation, TLS verification
INF-04	Resource Exhaustion	CV/LLM	DoS via oversized inputs, memory bombs, concurrent request flooding
INF-05	GPU Security	CV/LLM	GPU isolation, memory leakage between tenants, side-channel vectors
INF-06	Model Theft/Extraction	CV/LLM	Model file access controls, serialization security, watermark verification

DAT -- Data Governance (5 tests)

ID	Test	Target	What It Does
DAT-01	Consent Verification	CV/LLM	Training data consent tracking, opt-out mechanism validation
DAT-02	PII Leakage	CV/LLM	PII density scanning in model outputs, memorization detection
DAT-03	Data Lineage	CV/LLM	Provenance tracking, transformation audit trails
DAT-04	Right to Erasure	CV/LLM	GDPR deletion verification, unlearning effectiveness
DAT-05	Data Minimization	CV/LLM	Collection scope audit, retention policy enforcement

---

Compliance Frameworks

Tessera maps every test result to specific requirements in major regulatory and compliance frameworks:

Framework	Coverage	Mapping
EU AI Act	Articles 9, 15, 71	Article-level compliance mapping for high-risk AI systems
NIST AI RMF	Govern, Map, Measure, Manage	Function and category mapping across all 4 functions
SOC 2	Trust Services Criteria	CC6, CC7, CC8 control mapping for AI-specific risks
ISO 27001:2022	Annex A controls	A.5 through A.8 control mapping for AI security
OWASP AI Top 10	Full coverage	Direct test-to-risk mapping for all 10 categories

# Generate a compliance report
tessera --config config.yaml --format json html docx

# The HTML report includes compliance mapping tabs for each framework
# The DOCX report includes an executive compliance summary

---

GitHub OAuth Setup

Tessera supports GitHub OAuth for user authentication. To configure:

1. Go to GitHub Settings > Developer Settings > OAuth Apps > New OAuth App
2. Set the Authorization callback URL to: http://localhost:8000/api/v1/auth/github/callback
3. Copy your Client ID and Client Secret
4. Add to your .env file:

TESSERA_GITHUB_CLIENT_ID=your-github-client-id
TESSERA_GITHUB_CLIENT_SECRET=your-github-client-secret
TESSERA_GITHUB_REDIRECT_URI=http://localhost:8000/api/v1/auth/github/callback
TESSERA_FRONTEND_URL=http://localhost:5173
TESSERA_AUTH_ENABLED=true

5. Restart the API server. The login page will now show a "Sign in with GitHub" button.

---

Connectors

Tessera connects to 13 model serving backends out of the box. Configure one or many in your config.yaml.

#	Connector	Type	Protocol	Use Case
1	NVIDIA Triton	CV	gRPC / HTTP	Production model serving for CV models
2	vLLM	LLM	OpenAI-compatible	Self-hosted LLM inference at scale
3	OpenAI	LLM	REST API	GPT-4o, GPT-4, o1 series
4	Anthropic	LLM	REST API	Claude 3.5 Sonnet, Claude 3 Opus
5	Google Vertex AI	LLM	REST API	Gemini 1.5 Pro, Gemini Ultra, PaLM 2
6	Ollama	LLM	REST API	Local LLM testing (Llama 3, Mistral, Phi, Gemma)
7	HuggingFace	LLM/CV	Inference API	Any model on HuggingFace Hub
8	AWS Bedrock	LLM	AWS SDK	Claude, Llama, Titan on AWS
9	Azure OpenAI	LLM	REST API	GPT models on Azure
10	Mistral AI	LLM	REST API	Mistral Large, Mixtral, Mistral 7B
11	LiteLLM	LLM	Proxy	Unified proxy to 100+ providers
12	Together AI	LLM	REST API	Hosted open-source models
13	Custom	Any	OpenAI-compatible	Any endpoint that speaks OpenAI format

# Example: Multiple connectors in one config
models:
  triton:
    url: "${TRITON_URL:-localhost:8000}"
    protocol: "http"
    models:
      - name: "yolov8-detector"
        arch: "YOLOv8"
        task: "detection"
        input_shape: [3, 640, 640]

  ollama:
    url: "http://localhost:11434"
    models:
      - name: "llama3"
        task: "chat"

  custom:
    - name: "my-rag-agent"
      url: "http://internal-api:8080"
      task: "llm-agent"
      api_format: "openai"

---

Architecture

                          +------------------+
                          |     Web UI       |
                          | React + Vite     |
                          | TailwindCSS      |
                          +--------+---------+
                                   |
                          +--------v---------+
                          |    REST API      |
                          |  FastAPI 0.109+  |
                          |  WebSocket       |
                          +---+---------+----+
                              |         |
                   +----------+    +----v-------+
                   |               |  Celery    |
                   |               |  Workers   |
            +------v------+       +----+-------+
            | PostgreSQL  |            |
            | SQLAlchemy  |    +-------v-------+
            | + Alembic   |   |  Scan Engine   |
            +-------------+    |  3-Phase Loop  |
                               +--+----+---+---+
                                  |    |   |
                     +------------+    |   +-------------+
                     |                 |                  |
              +------v------+  +------v------+   +-------v-----+
              |  32 OWASP   |  | Connectors  |   |   Reports   |
              |   Tests     |  | (13 types)  |   | JSON/HTML/  |
              | MOD|APP|INF |  | Triton/vLLM |   |    DOCX     |
              | |DAT        |  | OpenAI/...  |   +-------------+
              +-------------+  +-------------+

                               +-------------+
                               |    Redis    |
                               | Task Queue  |
                               +-------------+

Project Structure

tessera/
+-- tessera/                    # Core package
|   +-- __init__.py             # v2.0.0, public API
|   +-- cli.py                  # CLI entry point (tessera)
|   +-- engine.py               # Scan engine (run_tests, run_per_model)
|   +-- config.py               # YAML loader with ${ENV_VAR} expansion
|   +-- registry.py             # 32-test registry + category mapping
|   +-- models.py               # Pydantic models (ScanRequest, ScanResult)
|   +-- reports.py              # JSON, HTML, DOCX report generation
|   +-- api/                    # FastAPI REST API
|   |   +-- app.py              # Application factory
|   |   +-- websocket.py        # Real-time scan progress
|   |   +-- routers/            # health, scans, models, results, reports, config, auth
|   |   +-- schemas/            # Request/response schemas
|   +-- db/                     # Database layer
|   |   +-- engine.py           # SQLAlchemy async engine
|   |   +-- models.py           # 7 ORM models
|   |   +-- crud/               # CRUD operations
|   |   +-- migrations/         # Alembic migrations
|   +-- worker/                 # Celery task workers
|   +-- enterprise/             # Licensed features
|       +-- auth/               # JWT + RBAC + SSO (OIDC) + GitHub OAuth
|       +-- compliance/         # EU AI Act, NIST AI RMF, SOC 2, ISO 27001
|       +-- multi_tenant/       # Org-based isolation middleware
|       +-- scheduling/         # Celery Beat recurring scans
|       +-- branding/           # White-label report customization
|       +-- audit/              # Action audit logging
+-- tests/                      # 32 OWASP test implementations
|   +-- base.py                 # OWASPTestCase ABC (3-phase runner)
|   +-- mod/                    # MOD-01 through MOD-07
|   +-- app/                    # APP-01 through APP-14
|   +-- inf/                    # INF-01 through INF-06
|   +-- dat/                    # DAT-01 through DAT-05
+-- test_suite/                 # 375 pytest unit/integration/e2e tests
+-- scripts/                    # Benchmark generation + utilities
+-- utils/                      # Connector wrappers + report renderers
+-- web/                        # React 18 + TypeScript + Vite UI
|   +-- src/components/         # Dashboard, Scans, Models, Results, Reports, Settings
+-- helm/tessera/               # Kubernetes Helm chart
+-- examples/                   # Example configs per connector
+-- docker-compose.yml          # Full-stack deployment
+-- Dockerfile                  # Multi-stage build (React + Python)
+-- pyproject.toml              # Package metadata + dependencies

---

Deployment

Tessera supports four deployment modes, from zero-infrastructure CLI to production Kubernetes.

Mode 1: CLI (Zero Infrastructure)

No database, no server -- just run scans from the terminal.

# Install
pip install tessera-ai

# Run all tests against your config
tessera --config config.yaml

# Run specific tests
tessera --config config.yaml --tests MOD-01 APP-01 INF-03

# Run by category
tessera --config config.yaml --category app

# Per-model mode (route tests to each model by type)
tessera --config config.yaml --per-model --format json html docx

# Filter by model type
tessera --config config.yaml --per-model --model-type llm

# Check available dependencies
tessera --check-deps

# List all 32 tests
tessera --list

Mode 2: API Server (FastAPI)

Full REST API with WebSocket progress streaming.

# Install server dependencies
pip install tessera-ai[server,reports]

# Start the API server
uvicorn tessera.api.app:create_app --factory --host 0.0.0.0 --port 8000

# API docs at http://localhost:8000/docs
# ReDoc at http://localhost:8000/redoc

Mode 3: Docker Compose (Full Stack)

API server + Celery workers + PostgreSQL + Redis in one command.

# Start everything
docker compose up -d

# With build
docker compose up -d --build

# Scale workers
docker compose up -d --scale worker=4

# View logs
docker compose logs -f api worker

Services started:

Service	Port	Description
api	8000	FastAPI server + static Web UI
worker	--	2x Celery workers for async scans
postgres	5432	PostgreSQL 16 (scan data, results, users)
redis	6379	Redis 7 (task queue, WebSocket pub/sub)
migrate	--	One-shot Alembic migration runner

Mode 4: Kubernetes (Helm)

Production-grade deployment with HPA, secrets, and ingress.

# Add the Helm repo
helm repo add tessera https://charts.tessera.dev
helm repo update

# Install with defaults
helm install tessera tessera/tessera

# Install with custom values
helm install tessera tessera/tessera \
  --set ingress.host=tessera.mycompany.com \
  --set ingress.tls=true \
  --set autoscaling.enabled=true \
  --set autoscaling.maxReplicas=10 \
  --set database.url=postgresql://user:pass@rds-host:5432/tessera

# Or from local chart
helm install tessera ./helm/tessera -f my-values.yaml

Helm chart includes:

• API Deployment with health checks
• Worker Deployment (configurable replicas)
• HPA (Horizontal Pod Autoscaler) with CPU-based scaling
• Alembic migration Job (runs before API starts)
• ConfigMap for application config
• Secret for database credentials
• Service + Ingress

---

Configuration

Tessera uses YAML configuration with environment variable expansion.

# config.yaml
project:
  name: "Production AI Audit"
  version: "1.0"
  author: "Security Team"
  environment: "production"

# Model endpoints to test
models:
  triton:
    url: "${TRITON_URL:-localhost:8000}"
    protocol: "http"
    models:
      - name: "face_detector"
        arch: "RetinaFace"
        task: "detection"
        input_shape: [3, 640, 640]
        num_classes: 2

  ollama:
    url: "${OLLAMA_URL:-http://localhost:11434}"
    models:
      - name: "llama3"
        task: "chat"

# Tunable thresholds per test
params:
  evasion:
    epsilon: [0.01, 0.05]
    attacks: ["FGSM", "PGD"]
    num_samples: 10
  injection:
    bypass_threshold: 0.05
  alignment:
    refusal_threshold: 0.95
  pii_leakage:
    pii_density_threshold: 0.01

# Report output
output:
  dir: "reports"
  format: ["json", "html", "docx"]

Environment variable syntax:

• ${VAR} -- required, fails if unset
• ${VAR:-default} -- uses default if VAR is unset

Example configs are provided in the examples/ directory:

File	Connector	Description
cv-triton.yaml	NVIDIA Triton	Multi-model CV security audit
llm-openai.yaml	OpenAI	GPT-4o security evaluation
llm-vllm.yaml	vLLM	Self-hosted LLM testing
llm-ollama.yaml	Ollama	Local LLM security scan
huggingface-inference.yaml	HuggingFace	Inference API testing
aws-bedrock.yaml	AWS Bedrock	Cloud LLM audit

---

API Server

The REST API provides full programmatic control over scans, models, results, and reports.

Key Endpoints

Method	Endpoint	Description
GET	/health	Health check
GET	/ready	Readiness probe (checks DB connectivity)
POST	/api/v1/scans	Create and start a new scan
GET	/api/v1/scans	List scans (paginated)
GET	/api/v1/scans/{id}	Get scan details and status
DELETE	/api/v1/scans/{id}	Delete a scan
GET	/api/v1/results	Query results with filtering
GET	/api/v1/results/{id}	Get detailed test result
GET	/api/v1/results/compare	Compare results across scans
GET	/api/v1/models	List registered models
POST	/api/v1/models	Register a new model
GET	/api/v1/reports/{scan_id}	Generate report (JSON/HTML/DOCX)
GET	/api/v1/config	Get current configuration
PUT	/api/v1/config	Update configuration
POST	/api/v1/auth/github	Initiate GitHub OAuth flow
GET	/api/v1/auth/github/callback	GitHub OAuth callback
WS	/ws/scans/{id}	Real-time scan progress via WebSocket

Create a scan via API

curl -X POST http://localhost:8000/api/v1/scans \
  -H "Content-Type: application/json" \
  -d '{
    "config_path": "config.yaml",
    "category": "app",
    "per_model": true,
    "model_type_filter": "llm",
    "phases": [1, 2, 3]
  }'

Stream progress via WebSocket

const ws = new WebSocket("ws://localhost:8000/ws/scans/<scan-id>");
ws.onmessage = (event) => {
  const progress = JSON.parse(event.data);
  console.log(`${progress.current_test}: ${progress.message}`);
  // { scan_id, current_test, tests_completed, tests_total, message, status }
};

Download a report

# JSON report
curl http://localhost:8000/api/v1/reports/<scan-id>?format=json -o report.json

# Interactive HTML report
curl http://localhost:8000/api/v1/reports/<scan-id>?format=html -o report.html

# Executive DOCX report
curl http://localhost:8000/api/v1/reports/<scan-id>?format=docx -o report.docx

Database Schema

PostgreSQL with 7 tables managed by SQLAlchemy ORM and Alembic migrations:

organizations ──< users ──< scans ──< scan_results
                               |
                            configs
                            models
                            audit_logs

When no TESSERA_DATABASE_URL is configured, the API runs in standalone mode using an in-memory store -- ideal for quick evaluations.

---

Web UI

Tessera ships with a modern web dashboard built on React 18 + TypeScript + Vite + TailwindCSS.

Pages

Page	Description
Dashboard	Security posture overview, pass/fail trends, recent scan activity
Scans	List all scans, create new scans, filter by status
Scan Detail	Real-time progress, per-test results, phase breakdown
Models	Model registry, connector status, last scan timestamps
Results	Cross-scan result comparison, regression detection, filtering
Reports	Generate and download JSON/HTML/DOCX reports
Settings	Configuration management, threshold tuning
Login	GitHub OAuth + email/password authentication

Tech Stack

• React 18 with React Router v6
• TanStack Query v5 for server state management
• Recharts for security score visualization
• Lucide React icon set
• TailwindCSS 3.4 for utility-first styling
• Vite 5 for fast dev server and builds
• TypeScript 5.3 for type safety

# Development
cd web
npm install
npm run dev    # Vite dev server on :5173

# Production (built into Docker image automatically)
npm run build  # Outputs to web/dist/

---

Report Formats

JSON -- CI/CD Integration

Machine-readable output for pipeline automation. Includes full phase details, metrics, and per-test status.

{
  "framework": "Tessera",
  "version": "2.0.0",
  "summary": { "total": 14, "pass": 11, "fail": 2, "warn": 1 },
  "tests": [
    {
      "test_id": "APP-01",
      "test_name": "Prompt Injection",
      "status": "PASS",
      "phases": [
        {
          "phase": 1,
          "name": "Attack Simulation",
          "metrics": [{ "name": "bypass_rate", "value": 0.02, "threshold_pass": 0.05 }]
        }
      ]
    }
  ]
}

HTML -- Interactive Dashboard

Self-contained single-file HTML report with:

• Sidebar navigation by test category
• Status filtering (PASS / FAIL / WARN / ERROR)
• Model x test matrix (per-model mode)
• Per-phase metric details with evidence
• Responsive design, works offline

DOCX -- Executive Reports

Professional Word documents with:

• Executive summary table (pass/fail/warn/error counts)
• Model x test matrix with percentage scores
• Per-test detailed findings with evidence
• Actionable recommendations with reference links
• Suitable for board presentations and compliance documentation

---

Enterprise Features

The Community edition includes all 32 tests, CLI, API server, Web UI, and all report formats. Enterprise features are unlocked with a TESSERA_LICENSE_KEY (JWT-based, no DRM, no call-home).

Feature	Community	Pro	Enterprise
32 OWASP AI tests	Yes	Yes	Yes
CLI + API + Web UI	Yes	Yes	Yes
JSON/HTML/DOCX reports	Yes	Yes	Yes
13 connectors	Yes	Yes	Yes
Docker + Kubernetes	Yes	Yes	Yes
Max models	10	100	Unlimited
JWT Auth + RBAC	--	Yes	Yes
GitHub OAuth	--	Yes	Yes
SSO (OIDC/SAML)	--	--	Yes
Multi-tenancy	--	--	Yes
Compliance mapping	--	Yes	Yes
Scheduled scans	--	Yes	Yes
Audit logging	--	Yes	Yes
White-label branding	--	--	Yes

Compliance Mapping

Enterprise maps each test result to specific requirements in:

• EU AI Act -- Article-level compliance mapping
• NIST AI RMF -- Function and category mapping (Govern, Map, Measure, Manage)
• SOC 2 -- Trust Services Criteria mapping
• ISO 27001 -- Annex A control mapping

RBAC Roles

Role	Permissions
admin	Full access: users, orgs, settings, scans, results
analyst	Create scans, view results, generate reports
viewer	Read-only access to results and reports

---

3-Phase Methodology

Every one of the 32 tests implements the OWASP 3-phase methodology:

 Phase 1: ATTACK          Phase 2: MEASURE         Phase 3: DEFEND
 ==================       ==================       ==================
 Simulate the threat      Quantify the impact      Validate mitigations
 - Adversarial inputs     - Threshold scoring      - Defense effectiveness
 - Injection payloads     - Statistical metrics    - Recommendations
 - Extraction attempts    - PASS / WARN / FAIL     - Evidence collection

Threshold-Based Scoring

Each metric defines pass and fail thresholds. The status is derived automatically:

# Example: Prompt injection bypass rate
Metric(
    name="bypass_rate",
    value=0.03,            # Measured value
    threshold_pass=0.05,   # Below this = PASS
    threshold_fail=0.15,   # Above this = FAIL
    operator="<",          # Lower is better
    unit="%",
    source="OWASP AITG-APP-01"
)
# Result: PASS (0.03 < 0.05)

Rollup logic: The overall test status is the worst status across all three phases. If any phase is FAIL, the test is FAIL. If any is ERROR, the test is ERROR.

---

Comparison with Alternatives

Feature	Tessera	Garak	Promptfoo	HiddenLayer	Protect AI
OWASP coverage	32 tests, 4 categories	LLM probes only	LLM evals only	Model scanning	Model scanning
CV model testing	Yes (Triton, ART, Foolbox)	No	No	Partial	Partial
LLM testing	Yes (14 APP tests)	Yes	Yes	No	Partial
Infrastructure tests	Yes (6 INF tests)	No	No	No	Partial
Data governance	Yes (5 DAT tests)	No	No	No	No
3-phase methodology	Attack+Measure+Defend	Probes only	Evals only	Scan only	Scan only
API server	FastAPI + WebSocket	No	No	SaaS only	SaaS only
Web UI	React dashboard	No	Basic	SaaS only	SaaS only
Self-hosted	Yes	Yes	Yes	No	No
Kubernetes Helm	Yes	No	No	N/A	N/A
Report formats	JSON + HTML + DOCX	JSON	JSON + HTML	PDF	PDF
Connectors	13	OpenAI-compatible	OpenAI-compatible	File upload	File upload
Compliance mapping	EU AI Act, NIST, SOC 2	No	No	Partial	Partial
Open source	Apache 2.0	Apache 2.0	MIT	Proprietary	Proprietary
Multi-tenancy	Yes (Enterprise)	No	No	Yes	Yes
Pricing	Free core + paid tiers	Free	Free + paid	SaaS pricing	SaaS pricing

---

Development

Prerequisites

• Python 3.10+
• Node.js 20+ (for Web UI)
• Docker and Docker Compose (optional)

Setup

# Clone
git clone https://github.com/tessera-ops/tessera.git
cd tessera

# Create virtualenv
python -m venv .venv && source .venv/bin/activate

# Install in editable mode with test dependencies
pip install -e ".[all,test]"

# Run the test suite (375 tests)
pytest

# Run with coverage
pytest --cov=tessera --cov=tests --cov-report=html

# Lint
pip install ruff
ruff check . --select E,F,I --ignore E501,F401,F841

Writing a New Test

Every test inherits from OWASPTestCase and implements three methods:

from tests.base import OWASPTestCase, PhaseResult, Metric

class MOD99NewTest(OWASPTestCase):
    TEST_ID = "MOD-99"
    TEST_NAME = "My New Security Test"
    CATEGORY = "Model Security"
    OWASP_REF = "AITG-MOD-99"
    TOOLS = ["MyTool"]

    def phase1_attack(self, config: dict) -> PhaseResult:
        # Simulate the attack
        ...
        return PhaseResult(phase=1, name="Attack", status="PASS",
                          evidence=["Attack simulated successfully"])

    def phase2_measure(self, config: dict) -> PhaseResult:
        # Measure with thresholds
        metric = Metric(name="attack_success_rate", value=0.02,
                       threshold_pass=0.05, threshold_fail=0.20,
                       operator="<", unit="%")
        return PhaseResult(phase=2, name="Measure", metrics=[metric])

    def phase3_defend(self, config: dict) -> PhaseResult:
        # Validate defense
        ...
        return PhaseResult(phase=3, name="Defend", status="PASS")

Register it in tessera/registry.py:

TEST_REGISTRY["MOD-99"] = ("tests.mod.mod99_new_test", "MOD99NewTest")

Contributing

See CONTRIBUTING.md for the full guide covering:

• Development environment setup
• Code style (ruff, type hints)
• Test requirements (every test needs unit tests)
• PR process and review checklist

---

Roadmap

v2.1 (Next)

• SARIF output format for GitHub/GitLab Security tab integration
• OpenTelemetry tracing for scan observability
• Test parallelization (concurrent test execution per model)
• Slack/Teams webhook notifications on scan completion

v2.2

• Agent security tests (tool-use validation, chain-of-thought manipulation)
• Multimodal model support (vision-language models)
• RAG pipeline testing (retriever poisoning, context window attacks)
• Scan diff and regression tracking across releases

v3.0

• Plugin architecture for community-contributed tests
• Distributed scan execution across multiple workers
• Real-time model monitoring (continuous security posture)
• SBOM (Software Bill of Materials) for AI components

---

FAQ

Do I need all the dependencies installed?

No. Tessera uses lazy imports. If a test requires a dependency that is not installed (e.g., torch for MOD-01), that test phase returns ERROR with a message telling you what to install. All other tests run normally. Install only what you need:

• pip install tessera-ai -- minimal (no CV/LLM-specific libraries)
• pip install tessera-ai[cv] -- adds ART, Foolbox, Triton client, PyTorch
• pip install tessera-ai[llm] -- adds Detoxify, Fairlearn
• pip install tessera-ai[all] -- everything

Can I use Tessera without a database?

Yes. The CLI mode requires zero infrastructure. The API server also works without a database by using an in-memory store. Just omit the TESSERA_DATABASE_URL environment variable. Results are lost on restart in this mode.

Which AI models does Tessera support?

Tessera supports all major AI providers: OpenAI (GPT-4o, GPT-4, o1), Anthropic (Claude 3.5 Sonnet, Claude 3 Opus), Google (Gemini 1.5 Pro, Gemini Ultra), Meta (Llama 3 70B, Llama 3 8B), Mistral AI (Mistral Large, Mixtral), AWS Bedrock, Azure OpenAI, HuggingFace, and any OpenAI-compatible endpoint. For CV models, Tessera works with NVIDIA Triton, TorchServe, and any model accessible via ART or Foolbox.

How do I test a model behind authentication?

Use environment variables in your config:

models:
  custom:
    - name: "internal-model"
      url: "${MODEL_API_URL}"
      task: "chat"
      api_format: "openai"
      headers:
        Authorization: "Bearer ${MODEL_API_TOKEN}"

Can I run only specific phases?

Yes. Use the --phases flag to run only certain phases:

# Only run attack simulation
tessera --config config.yaml --phases 1

# Only measure and defend (skip attack)
tessera --config config.yaml --phases 2 3

How does per-model mode work?

With --per-model, Tessera enumerates all models from your config, determines each model's type (CV or LLM), and runs only the applicable tests for each model. CV models get MOD-01 through MOD-06 + INF + DAT tests. LLM models get MOD-07 + all APP + INF + DAT tests. Results are organized per-model with an executive summary including a model x test matrix.

Is there CI/CD integration?

Yes. Use JSON output + exit codes:

# GitHub Actions example
- name: Security scan
  run: |
    pip install tessera-ai[llm]
    tessera --config config.yaml --category app --format json
    # Exit code is non-zero if any test FAILs

What is the difference between APP-04 and APP-13?

Both address overreliance but from different angles. APP-04 tests factual accuracy, citation verification, and confidence calibration (does the model know what it does not know?). APP-13 tests user dependency patterns and guardrail bypass through trust exploitation (can an attacker leverage the user's trust in the model?).

---

License

Apache License 2.0 -- see LICENSE for the full text.

The Community edition includes all 32 tests, CLI, API server, Web UI, Docker, Helm, and all connectors. Enterprise features (auth, SSO, multi-tenancy, compliance mapping, scheduled scans, audit logging, white-label branding) require a commercial license.

---

Acknowledgments

Tessera builds on the work of these outstanding projects and standards:

• OWASP AI Testing Guide -- the test methodology and taxonomy that defines our 32 tests
• IBM Adversarial Robustness Toolbox (ART) -- adversarial attack and defense implementations
• Foolbox -- adversarial perturbation library
• Detoxify -- toxicity detection for LLM outputs
• Fairlearn -- fairness assessment metrics
• Cleanlab -- training data quality and label error detection
• Evidently AI -- data and model drift monitoring
• Garak -- LLM vulnerability scanning (inspiration for APP tests)
• Promptfoo -- LLM red-teaming (inspiration for prompt injection patterns)

---

Built for security teams who protect AI systems in production.
Test your GPT-4, Claude, Gemini, Llama, and Mistral deployments before attackers do.

GitHub • Issues • Discussions • Contributing