AI system hacking methodology
We follow a structured offensive-AI methodology aligned with MITRE ATLAS and the OWASP LLM & ML Top 10 (2025). Before testing, we scope your models, applications, RAG pipelines and agents, and classify the relevant attack taxonomy.
AI reconnaissance & attack surface mapping
Using AI-focused OSINT, we identify and enumerate AI assets, data pipelines, models, vector stores, endpoints and exposed parameters — the attack surface a real adversary would map first.
Vulnerability scanning & fuzzing
AI-specific vulnerability assessment and fuzzing across model interfaces, pipelines and deployments to surface weaknesses proactively and feed them into your security workflow.
Prompt injection & LLM application attacks
Direct and indirect prompt injection, jailbreaking, system-prompt leakage, sensitive-information disclosure and insecure output handling against real-world LLM applications.
Adversarial ML & model privacy
Adversarial input attacks across modalities, plus membership inference, model inversion and model-extraction attacks to evaluate robustness, trustworthiness and privacy.
Data & training pipeline attacks
Data poisoning and backdoor/trojan insertion targeting training pipelines and model integrity, with measures to safeguard your data supply chain.
Agentic AI & model-to-model attacks
Excessive-agency exploitation, cross-LLM and orchestration abuse, tool/plugin misuse and denial-of-wallet (unbounded resource consumption) against autonomous agents.
AI infrastructure & supply chain
Offensive testing of AI frameworks, deployment pipelines, plugins, APIs and third-party dependencies, followed by hardening of the AI infrastructure and supply chain.
Frameworks & standards
What you get
- Reproducible attack traces and proof-of-concept exploits
- Prioritized findings optionally mapped to MITRE ATLAS and OWASP LLM Top 10
- Hardening guidance for guardrails, RAG pipelines and plugins
- AI incident-response and forensics readiness notes (add-on)
- Executive summary and audit-ready evidence for EU AI Act / NIS 2 / DORA
FAQ
Which AI systems do you test?
LLM apps and chatbots, RAG pipelines, multi-agent/agentic systems, copilots and AI-enabled APIs — cloud-hosted or self-hosted.
Do you need access to the model?
We test black-box via the application/API and, where useful, grey/white-box with access to prompts, tools and pipeline configuration.
How does this map to compliance?
Findings are mapped to EU AI Act, NIS 2 and DORA evidence requirements so they slot directly into your governance and audit process.