ARES tests your LLMs for prompt injection, data leakage, model theft, and the full OWASP Top 10 for Large Language Models. Every finding includes exploit code and a fix.
Direct and indirect injection attacks across all input channels — system prompts, user messages, retrieved context, and tool outputs.
Detect PII, credentials, internal URLs, and proprietary data leakage through model outputs, embeddings, and training data extraction.
Architecture fingerprinting, model stealing via distillation, membership inference attacks, and surrogate model accuracy assessment.
GCG-optimized suffixes, gradient-based attacks, character-level perturbations, and embedding-space adversarial examples.
Tests retrieval-augmented generation pipelines for context poisoning, document injection, and cross-user data leakage in vector databases.
Probes content filters, safety classifiers, and output guards with evasion techniques that adapt to each model's defenses.
Tests across OpenAI, Anthropic, Google, Azure, Mistral, DeepSeek, Groq, and local Ollama models to catch provider-specific vulnerabilities.
Attack playbooks for common AI security scenarios: jailbreaks, role-play attacks, encoding tricks, and recursive extraction.
Findings with prompt-level evidence, CVSS scoring, LLM-specific guidance, and code-level fixes for guardrail implementation.
ARES tests against every category in the OWASP Top 10 for Large Language Model Applications — with automated exploit generation, evidence capture, and remediation guidance for each finding.
| Technique | Severity |
|---|---|
Direct Prompt Injection | Critical |
Indirect / Stored Injection | Critical |
Jailbreak Attacks | High |
Data Exfiltration | Critical |
Model Inversion | High |
Adversarial Suffix | High |
Prompt Leaking | High |
Denial of Wallet | Medium |
ARES applies the same autonomous agent loop to LLM-powered applications — discovering, probing, and verifying vulnerabilities across the entire AI stack.
Map all LLM endpoints, model providers, RAG pipelines, embedding databases, and agent configurations. Identify every entry point for AI-specific attacks.
Launch automated attacks across 8+ technique classes: prompt injection, jailbreaks, data extraction, adversarial inputs, denial of wallet, and supply chain probes.
Each finding is independently replayed and verified. Severity is scored using LLM-specific CVSS modifiers for business impact, reproducibility, and exploitability.
Generate guardrail code, input sanitization rules, and output filter configurations. Continuous monitoring detects regressions and new attack patterns over time.
Injection testing across all input modalities with automated payload generation and evasion techniques.
Detect every form of data leakage from training data, context windows, and model outputs.
Evaluate model integrity, architecture, and resistance to extraction and inversion attacks.
Tests content filters, safety classifiers, and output guardrails for completeness. Finds the gaps attackers will find.
Test the broader AI supply chain — from model provenance to deployment infrastructure.
Start testing your LLM-powered applications against the full spectrum of AI security threats — from prompt injection to model theft — before attackers do.