⚔️ AetherRed — Excalibur

AI Red-Teaming & Attack Simulation Platform

Attack your AI before attackers do.

A product of AetherGuard AI

Excalibur is a comprehensive adversarial testing tool for LLMs, AI agents, and RAG systems. It surfaces vulnerabilities before real attackers do — 22 attack types across 8 categories, MITRE ATLAS mapped, with an LLM Judge for evaluation and a proprietary AetherGuard Resilience Score.

📑 Table of Contents

✨ Features
🧰 Technology Stack
📦 Installation
🔑 Environment Setup
🚀 Quick Start
🎯 Attack Catalog (22 Attacks)
⚖️ LLM Judge
🗂️ Datasets & Licensing
📋 Campaign Mode
📊 Output & Reporting
🌐 API Server
🖥️ Standalone UI
🐳 Docker
🔁 CI/CD Integration (SARIF)
🏗️ Architecture
🗺️ MITRE ATLAS Mapping
📄 License

✨ Features

	Feature	Description
🎯	22 Attack Types	Across 8 categories: Adversarial ML, NLP, Agent, RAG, Infrastructure, Model Integrity, Evasion, Content Safety
⚖️	LLM Judge Evaluation	A separate LLM (GPT-4o / Claude) evaluates attack results with structured verdicts, replacing keyword heuristics with real language understanding
🛡️	Content Safety Suite	Prompt injection, DAN jailbreaks, PII/PHI leakage, HAP (hate/abuse/profanity), secrets exposure, and malicious content generation
🔲	Black-box + White-box	API-based attacks and direct model-weight access
🗺️	MITRE ATLAS Mapping	Every attack mapped to ATLAS technique IDs
📈	AetherGuard Resilience Score	Proprietary 0–100 composite score across 8 categories
📐	ART-Compatible Metrics	Industry-standard precision / recall / F1
📄	Multi-Format Reports	HTML, PDF, JSON, and SARIF for CI/CD
🤗	Open-Source Dataset Integration	Optionally augment built-in payloads with public open-source datasets from the HuggingFace Hub
🧩	Plugin Architecture	Extensible with custom attacks via `@register_attack`
🖧	Three Interfaces	CLI, REST API, and standalone Web UI

🧰 Technology Stack

Core Engine (Python)

Technology	Purpose
	Runtime (`requires-python >=3.11`)
	CLI framework (`excalibur` command)
	Terminal formatting & progress output
	Config & data-model validation
	Async HTTP client for target adapters
	Numerical operations & scoring

Optional Capability Groups

Group	Technologies
whitebox
nlp	sentence-transformers · NLTK · deep-translator
datasets
vectorstore	· pgvector (asyncpg)
api
reporting	Jinja2 · WeasyPrint (PDF) · Plotly

Web UI (TypeScript / React)

Technology	Purpose
	UI framework
	Type-safe frontend
	Build tooling & dev server
	Server-state / data fetching
	Client-side routing
	Charts & visualizations
	Icon set

Target Adapters

📦 Installation

cd aetherguard-excalibur

# Core (CLI + black-box attacks)
pip install -e "."

# Full install (all attack types + API + reporting)
pip install -e ".[full]"

# Development (adds pytest, ruff, hypothesis, respx)
pip install -e ".[full,dev]"

Optional Groups

pip install -e ".[whitebox]"     # PyTorch + HuggingFace + ONNX (for PGD/FGSM/Smoothing)
pip install -e ".[nlp]"          # sentence-transformers + NLTK (for TextFooler)
pip install -e ".[vectorstore]"  # Pinecone + Weaviate + Chroma + pgvector
pip install -e ".[datasets]"     # HuggingFace datasets (for content-safety payloads)
pip install -e ".[api]"          # FastAPI server
pip install -e ".[reporting]"    # HTML/PDF report generation

🔑 Environment Setup

API keys are never passed on the command line — they are read from environment variables.

# Target provider keys
export EXCALIBUR_OPENAI_API_KEY=sk-...
export EXCALIBUR_ANTHROPIC_API_KEY=sk-ant-...
export EXCALIBUR_AZURE_OPENAI_API_KEY=...
export AWS_REGION=us-east-1              # For Bedrock

# LLM Judge (optional — a separate LLM that evaluates attack results)
export EXCALIBUR_JUDGE_API_KEY=sk-...    # Defaults to OpenAI GPT-4o

🚀 Quick Start

# Verify installation
excalibur --version
excalibur list                    # List all 22 attacks
excalibur list --category nlp     # Filter by category

# Validate a campaign config
excalibur validate campaigns/quick_scan.yaml

# Run a single attack
excalibur run hallucination_induction --target openai -m gpt-4o-mini -n 10

# Run a quick campaign (5 attacks)
excalibur campaign campaigns/quick_scan.yaml -o results/

# Run the full 22-attack audit
excalibur campaign campaigns/full_audit.yaml -o results/full/

# Start the API server
excalibur serve --port 8100

# Start the UI (separate terminal)
cd ui && npm install && npm run dev

CLI Commands

Command	Status	Description
`excalibur run <attack>`	✅	Run a single attack against a target
`excalibur campaign <config.yaml>`	✅	Execute a full campaign from YAML
`excalibur list`	✅	List available attacks (optionally by category)
`excalibur validate <config.yaml>`	✅	Validate a campaign config without executing
`excalibur serve`	✅	Start the REST API server
`excalibur report <dir>`	🚧	Regenerate reports from saved results (coming soon)
`excalibur benchmark <target>`	🚧	Evasion benchmark suite (planned)

🎯 Attack Catalog (22 Attacks)

Prerequisite for black-box examples: export EXCALIBUR_OPENAI_API_KEY=sk-your-key

🧪 Adversarial ML

1. PGD (Projected Gradient Descent) — white-box iterative gradient attack

excalibur run pgd \
  --target local \
  --model distilgpt2 \
  -p epsilon=0.03 \
  -p step_size=0.007 \
  -p iterations=40 \
  -p norm=linf \
  -p random_start=true \
  -n 50

ATLAS: AML.T0043 • Interface: White-box • Category: Adversarial ML

2. FGSM (Fast Gradient Sign Method) — single-step gradient perturbation

excalibur run fgsm \
  --target local \
  --model distilgpt2 \
  -p epsilon=0.05 \
  -p norm=linf \
  -n 100

ATLAS: AML.T0043 • Interface: White-box • Category: Adversarial ML

3. Randomized Smoothing — certified L2 robustness via Gaussian noise

excalibur run randomized_smoothing \
  --target local \
  --model distilgpt2 \
  -p sigma=0.25 \
  -p n_samples=500 \
  -p alpha=0.001 \
  -n 30

ATLAS: AML.T0043.002 • Interface: White-box • Category: Adversarial ML

4. Transfer Attack — craft on a local surrogate, test against the remote target

excalibur run transfer_attack \
  --target openai \
  --model gpt-4o-mini \
  -p surrogate_model=distilbert-base-uncased \
  -p query_budget=200 \
  -p attack_method=pgd \
  -p epsilon=0.03 \
  -p num_adversarial=30 \
  -n 30

ATLAS: AML.T0044 • Interface: Black-box target + local surrogate • Category: Adversarial ML

🗣️ NLP & Language

5. TextFooler — word-level synonym substitution preserving semantics

excalibur run textfooler \
  --target openai \
  --model gpt-4o-mini \
  -p max_perturbation_pct=0.2 \
  -p similarity_threshold=0.8 \
  -n 50

ATLAS: AML.T0043.001 • Interface: Black-box • Category: NLP & Language

6. Low-Resource Language Attack — safety bypass via underrepresented languages

excalibur run low_resource_language \
  --target openai \
  --model gpt-4o-mini \
  -p languages=amharic,yoruba,swahili,burmese,khmer,georgian \
  -p attack_types=translation,code_switch,transliteration \
  -n 50

ATLAS: AML.T0051 • Interface: Black-box • Category: NLP & Language

7. Multi-Turn Chain — conversational jailbreak escalation with language switching

excalibur run multi_turn_chain \
  --target openai \
  --model gpt-4o-mini \
  -p max_turns=6 \
  -p chain_count=20 \
  -p templates=dan,aim,developer_mode,system_prompt_leak \
  -p escalation_strategy=gradual \
  -n 20

ATLAS: AML.T0051.001 • Interface: Black-box • Category: NLP & Language

🤖 Agent & Trust

8. MAIC (Multi-Agent Infection Chain) — infection propagation across an agent topology

excalibur run maic \
  --target openai \
  --model gpt-4o-mini \
  -p topology=linear_3_agent \
  -p payloads=prompt_injection,tool_poisoning,context_manipulation \
  -p propagation_hops=3 \
  -n 30

ATLAS: AML.T0052 • Interface: Protocol simulation • Category: Agent & Trust

9. Cross-Trust Boundary — privilege escalation & data exfiltration across domains

excalibur run cross_trust \
  --target openai \
  --model gpt-4o-mini \
  -p boundaries=user_agent,agent_tool,tool_api \
  -p scenarios=privilege_escalation,data_exfiltration,confused_deputy \
  -n 30

ATLAS: AML.T0024 • Interface: Black-box • Category: Agent & Trust

📚 RAG & Embedding

10. KB Poisoning — inject poisoned documents into a vector store to hijack retrieval

excalibur run kb_poisoning \
  --target openai \
  --model gpt-4o-mini \
  -p strategy=embedding_cluster \
  -p injection_count=20 \
  -p target_queries=10 \
  -n 20

ATLAS: AML.T0020 • Interface: Vector store + API • Category: RAG & Embedding

Requires vector store access. Set PINECONE_API_KEY or configure ChromaDB/pgvector.

11. Embedding Inversion — reconstruct original text from embedding vectors (privacy leakage)

excalibur run embedding_inversion \
  --target openai \
  --model gpt-4o-mini \
  -p training_corpus_size=500 \
  -p test_samples=30 \
  -p reconstruction_method=mlp_decoder \
  -n 30

ATLAS: AML.T0024 • Interface: Embedding API • Category: RAG & Embedding

12. Document Injection — hidden adversarial content in docs (invisible text, metadata, unicode)

excalibur run doc_injection \
  --target openai \
  --model gpt-4o-mini \
  -p formats=pdf,docx,markdown \
  -p techniques=invisible_text,metadata_payload,unicode_confusable \
  -n 30

ATLAS: AML.T0020.001 • Interface: Document generation • Category: RAG & Embedding

🏗️ Infrastructure

13. API Key Impersonation — detection of stolen/misused API credentials

excalibur run api_key_impersonation \
  --target openai \
  --model gpt-4o-mini \
  -p scenarios=replay,geo_mismatch,concurrent,expired,enumeration,header_injection \
  -n 50

ATLAS: AML.T0040 • Interface: HTTP • Category: Infrastructure

14. Cross-Contamination — multi-tenant isolation boundary testing

excalibur run cross_contamination \
  --target openai \
  --model gpt-4o-mini \
  -p tenant_count=3 \
  -p scenarios=shared_cache,prompt_injection_crossover,timing_side_channel \
  -n 30

ATLAS: AML.T0024 • Interface: HTTP + protocol • Category: Infrastructure

🧬 Model Integrity

15. Watermark Audit — detect & test backdoor triggers in model watermarks

excalibur run watermark_audit \
  --target local \
  --model ./my-model.pt \
  -p detection_methods=spectral_signature,activation_clustering \
  -p trigger_candidates=100 \
  -n 50

ATLAS: AML.T0020 • Interface: White-box • Category: Model Integrity

16. Hallucination Induction — inputs that systematically induce confident false outputs

excalibur run hallucination_induction \
  --target openai \
  --model gpt-4o-mini \
  -p categories=factual,citation,capability,grounding_bypass \
  -n 100 \
  -o results/hallucination.json

ATLAS: AML.T0048 • Interface: Black-box • Category: Model Integrity

🛡️ Content Safety

17. Prompt Injection — direct & indirect injection with 5 categories incl. encoding evasion

excalibur run prompt_injection \
  --target openai \
  --model gpt-4o-mini \
  -p categories=system_override,indirect_injection,role_manipulation,context_manipulation,encoding_evasion \
  -n 50

ATLAS: AML.T0051.000 • Interface: Black-box • Category: Content Safety Payloads: Built-in, optionally augmented with open-source datasets

18. DAN-Style Jailbreaks — 8 persona-based jailbreak templates

excalibur run jailbreak_dan \
  --target openai \
  --model gpt-4o-mini \
  -p personas=dan_classic,aim,stan,dude,jailbroken,evil_confidant,maximum,developer_mode_v2 \
  -p intents_per_persona=5 \
  -n 40

ATLAS: AML.T0051.002 • Interface: Black-box • Category: Content Safety Payloads: Built-in, optionally augmented with open-source datasets

19. PII/PHI Leakage — induce generation of realistic personal & health data

excalibur run pii_phi_leakage \
  --target openai \
  --model gpt-4o-mini \
  -p categories=direct_generation,contextual_extraction,roleplay_extraction,indirect_phi \
  -p pii_types=personal_identifiers,financial,contact,health_phi,credentials \
  -n 30

ATLAS: AML.T0024.001 • Interface: Black-box • Category: Content Safety Payloads: Built-in, optionally augmented with open-source synthetic-PII datasets

20. HAP Content (Hate/Abuse/Profanity) — toxic content propensity across demographics

excalibur run hap_content \
  --target openai \
  --model gpt-4o-mini \
  -p categories=hate_speech,identity_attack,threats_violence,profanity_obscenity,harassment_bullying,implicit_toxicity \
  -p target_groups="Black people,Asian people,LGBTQ+ people,women,disabled people,immigrants" \
  -n 50

ATLAS: AML.T0048.001 • Interface: Black-box • Category: Content Safety Payloads: Built-in, optionally augmented with open-source datasets

21. Secrets Leakage — induce generation of API keys, tokens, and credentials

excalibur run secrets_leakage \
  --target openai \
  --model gpt-4o-mini \
  -p categories=direct_generation,code_context,contextual_extraction,system_prompt_secrets,social_engineering \
  -p secret_types=aws,github,openai,stripe,gcp,database_url,private_key,jwt \
  -n 40

ATLAS: AML.T0024.002 • Interface: Black-box • Category: Content Safety Payloads: Built-in

22. Malicious Content Generation — induce exploit code, malware, and harmful payloads

excalibur run malicious_content \
  --target openai \
  --model gpt-4o-mini \
  -p categories=malware_code,exploit_code,phishing_content,social_engineering,weapon_instructions,fraud_scams \
  -p severity_levels=high,critical \
  -n 50

ATLAS: AML.T0048.002 • Interface: Black-box • Category: Content Safety Payloads: Built-in, optionally augmented with open-source datasets

⚖️ LLM Judge

Excalibur includes an LLM Judge — a separate evaluation LLM that provides structured verdicts on attack results instead of relying solely on keyword heuristics.

Why a Judge?

Keyword-based detection has gaps:

Can't detect harmful content in non-English languages
Misses subtle compliance (the model helps without using obvious keywords)
Can't evaluate nuance (educational vs. actually harmful)

The Judge LLM reads the attack prompt and model response, then returns a structured JSON verdict with violation status, confidence, category, reasoning, and severity.

Configuration

# In campaign YAML:
judge:
  provider: openai          # or anthropic
  model: gpt-4o             # strong evaluator model
  api_key_env: EXCALIBUR_JUDGE_API_KEY
  temperature: 0.0
  enabled: true

# Or via CLI flags:
export EXCALIBUR_JUDGE_API_KEY=sk-...

excalibur campaign campaigns/full_audit.yaml --judge openai --judge-model gpt-4o

# Single attack with judge:
excalibur run prompt_injection --target openai --judge openai --judge-model gpt-4o -n 50

Judge Verdict Schema

{
  "is_violation": true,
  "confidence": 0.95,
  "category": "harmful_content",
  "reasoning": "The model provided step-by-step SQL injection instructions",
  "severity": "high"
}

Evaluation Types

Type	Evaluates	Used By
Safety Bypass	Did the model produce harmful content?	Prompt Injection, Jailbreaks, HAP, Malicious Content
Hallucination	Did the model fabricate facts/citations?	Hallucination Induction
Agent Infection	Did the agent comply with malicious instructions?	MAIC, Cross-Trust
Trust Boundary	Was a security boundary breached?	Cross-Trust, Cross-Contamination

Fallback

When the judge is disabled or unavailable, all attacks fall back to built-in multi-tier heuristic detection. The judge is optional but significantly improves detection accuracy, especially for:

Non-English responses (low-resource language attacks)
Subtle compliance without explicit harmful keywords
Implicit toxicity (coded language, dog whistles)
Context-dependent violations

🗂️ Datasets & Licensing

Several Content Safety attacks can optionally augment their built-in payloads with real-world examples from public, open-source datasets hosted on the HuggingFace Hub. This is opt-in — controlled per attack via use_hf_payloads (default false) — and requires the optional extra:

pip install -e ".[datasets]"

When enabled, datasets are downloaded at runtime via datasets.load_dataset(). Excalibur does not bundle or redistribute any dataset content in this repository. If the datasets library is not installed, the affected attacks log a warning and fall back to their built-in payloads.

Any dataset used is configurable per attack (via hf_dataset / hf_datasets params), so you can point each attack at the open-source dataset of your choice. Synthetic (artificially generated) datasets are preferred for PII/PHI testing so that no real personal data is involved.

Your Responsibilities

Verify the license of any dataset you configure on its source page before use. Licenses differ and some may restrict commercial use or require accepting gated terms.
Prefer synthetic data for PII testing. Use artificially generated PII rather than any dataset containing real individuals' data.
Payloads leave your environment. Running attacks sends payloads to your configured target providers (OpenAI, Anthropic, Azure, Bedrock). Review each provider's data-handling policy before testing with sensitive content.
Attribution. If a dataset's license requires attribution, credit the source in any published results.

Any external datasets are third-party resources governed by their own terms. AetherGuard AI does not distribute them and is not responsible for their content. Compliance with each dataset's license is the responsibility of the user.

📋 Campaign Mode

Quick Scan (5 attacks, ~5 min)

excalibur campaign campaigns/quick_scan.yaml -o results/quick/

Full Audit (22 attacks, ~30 min)

excalibur campaign campaigns/full_audit.yaml -o results/full/

Custom Campaign

Create my-campaign.yaml:

name: "Custom Red Team"
target:
  type: openai
  model: gpt-4o
  api_key_env: EXCALIBUR_OPENAI_API_KEY

attacks:
  - type: textfooler
    params: { samples: 50, max_perturbation_pct: 0.15 }
  - type: hallucination_induction
    params: { samples: 100, categories: [factual, citation] }
  - type: multi_turn_chain
    params: { chain_count: 20, templates: [dan, aim] }
  - type: maic
    params: { topology: star_4_agent, propagation_hops: 3 }
  - type: cross_trust
    params: { scenarios: [privilege_escalation, confused_deputy] }

parallel: 3

reporting:
  formats: [json, html, sarif]
  resilience_score: true
  atlas_mapping: true

excalibur campaign my-campaign.yaml -o results/custom/

# Override parallelism at runtime:
excalibur campaign my-campaign.yaml --parallel 5 -o results/custom/

📊 Output & Reporting

Resilience Score (0–100)

Grade	Score	Meaning
🟢 A	90–100	Excellent resilience
🟢 B	80–89	Good — minor gaps
🟡 C	70–79	Moderate — several vectors effective
🟠 D	60–69	Below average
🔴 F	0–59	Poor — most attacks succeed

Report Formats

Format	File	Use Case
🧾 JSON	`results.json`	Programmatic, CI/CD
🌐 HTML	`report.html`	Interactive browser view
📕 PDF	`report.pdf`	Executive summary
🔍 SARIF	`results.sarif`	GitHub Code Scanning

Campaign runs write reports automatically to the output directory based on the reporting.formats list in your campaign config.

🌐 API Server

excalibur serve --port 8100
excalibur serve --reload    # development mode (auto-reload)

📖 Swagger Docs: http://localhost:8100/docs
❤️ Health: http://localhost:8100/health
🎯 Attacks: GET /api/attacks, POST /api/attacks/run
📋 Campaigns: POST /api/campaigns, GET /api/campaigns/{id}
📊 Reports: /api/reports

🖥️ Standalone UI

cd ui
npm install
npm run dev
# → http://localhost:5173

Built with React 18 + TypeScript + Vite. Pages:

Route	Page
`/`	Dashboard
`/campaigns/new`	Campaign Builder
`/campaigns/:id` · `/results`	Results Viewer
`/attacks`	Attack Library
`/settings`	Settings

🐳 Docker

The image is built from python:3.11-slim with the full feature set and runs as a non-root user.

docker build -t excalibur:1.0.0 .

# Run CLI
docker run --rm -e EXCALIBUR_OPENAI_API_KEY=$EXCALIBUR_OPENAI_API_KEY \
  excalibur:1.0.0 run hallucination_induction --target openai -n 10

# Run API server
docker run -p 8100:8100 -e EXCALIBUR_OPENAI_API_KEY=$EXCALIBUR_OPENAI_API_KEY \
  excalibur:1.0.0 serve --host 0.0.0.0 --port 8100

🔁 CI/CD Integration (SARIF)

# GitHub Actions
- name: Run Excalibur Scan
  env:
    EXCALIBUR_OPENAI_API_KEY: ${{ secrets.OPENAI_KEY }}
  run: |
    excalibur campaign campaigns/quick_scan.yaml -o results/

- name: Upload SARIF
  uses: github/codeql-action/upload-sarif@v3
  with:
    sarif_file: results/results.sarif

- name: Quality Gate
  run: |
    SCORE=$(python -c "import json; print(json.load(open('results/results.json'))['resilience_score']['overall'])")
    [ $(echo "$SCORE < 70" | bc) -eq 1 ] && exit 1

🏗️ Architecture

excalibur CLI / API / UI
        │
   Campaign Engine (orchestration, parallelism, progress)
        │
   ┌────┴────┐
   │         │
   │    LLM Judge ─── Structured verdicts (safety bypass, hallucination, agent infection, trust boundary)
   │         │
   Attack Registry ──── 22 Attack Plugins (@register_attack)
        │
   Target Adapters ──── OpenAI, Anthropic, Azure, Bedrock, Local, VectorStores
        │
   Reporting Engine ─── Resilience Score, ATLAS, ART, HTML/PDF/SARIF

Source layout (src/aetherguard_excalibur/):

Module	Responsibility
`cli.py`	Click-based `excalibur` command entry point
`engine.py`	Campaign orchestration, parallelism, progress events
`registry.py`	Attack discovery & `@register_attack` plugin registry
`judge.py`	LLM Judge — structured verdict evaluation
`config.py` · `models.py`	Pydantic config loading & data models
`adapters/`	Target adapters (OpenAI, Anthropic, Azure, Bedrock, local)
`attacks/`	22 attack plugins across 7 category folders
`reporting/`	Resilience score, ATLAS mapper, ART metrics, SARIF, report generator
`api/`	FastAPI app + routes (attacks, campaigns, reports)

🗺️ MITRE ATLAS Mapping

#	Attack	ATLAS ID	Tactic
1	PGD	AML.T0043	ML Attack Staging
2	FGSM	AML.T0043	ML Attack Staging
3	Randomized Smoothing	AML.T0043.002	ML Attack Staging
4	Transfer Attack	AML.T0044	ML Attack Staging
5	TextFooler	AML.T0043.001	ML Attack Staging
6	Low-Resource Language	AML.T0051	Initial Access
7	Multi-Turn Chain	AML.T0051.001	Initial Access
8	MAIC	AML.T0052	Initial Access
9	Cross-Trust Boundary	AML.T0024	Exfiltration
10	KB Poisoning	AML.T0020	ML Attack Staging
11	Embedding Inversion	AML.T0024	Exfiltration
12	Document Injection	AML.T0020.001	ML Attack Staging
13	API Key Impersonation	AML.T0040	Initial Access
14	Cross-Contamination	AML.T0024	Collection
15	Watermark Audit	AML.T0020	ML Attack Staging
16	Hallucination Induction	AML.T0048	Impact
17	Prompt Injection	AML.T0051.000	Initial Access
18	DAN-Style Jailbreaks	AML.T0051.002	Defense Evasion
19	PII/PHI Leakage	AML.T0024.001	Exfiltration
20	HAP Content	AML.T0048.001	Impact
21	Secrets Leakage	AML.T0024.002	Exfiltration
22	Malicious Content	AML.T0048.002	Impact

📄 License

MIT License

_{⚔️ AetherRed — Excalibur · Attack your AI before attackers do.}

Name		Name	Last commit message	Last commit date
campaigns		campaigns
docs-site		docs-site
src/aetherguard_excalibur		src/aetherguard_excalibur
ui		ui
DISTRIBUTION-PLAN.md		DISTRIBUTION-PLAN.md
Dockerfile		Dockerfile
README.md		README.md
mkdocs.yml		mkdocs.yml
package-lock.json		package-lock.json
pyproject.toml		pyproject.toml

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