AI & Machine Learning Security Testing
Your AI Stack Has Attack Surfaces Your Security Team Has Never Tested.
Every large language model integration, every GenAI-powered application, every autonomous agent pipeline, and every MLSecOps environment you deploy introduces a new class of vulnerability — one that does not appear on traditional penetration testing checklists and that most offensive security teams are not equipped to assess.
Evaluris conducts adversarial security testing of AI systems, LLM-integrated applications, and ML infrastructure aligned to OWASP Top 10 for LLM Applications, OWASP ML Security Top 10, and the MITRE ATLAS adversarial machine learning threat matrix. Our operators have published original research on system prompt poisoning, autonomous APT frameworks, and AI-driven attack orchestration — applied directly to every engagement.
The AI Security Gap in 2026
Organizations are deploying AI faster than security teams can assess it. LLM-integrated customer support systems, AI-powered security tooling, autonomous agents with access to internal databases and APIs, and GenAI document processing pipelines are all in production — and virtually none of them have been tested by an adversary who understands how these systems can be manipulated.
The OWASP Top 10 for LLM Applications exists because these vulnerabilities are real, consistently exploitable, and consistently missed by general-purpose penetration testers who approach AI systems with web application methodology. Prompt injection is not a configuration problem. Model extraction is not a theoretical concern. Agentic tool abuse is not a future risk — it is happening in production environments today.
Evaluris operators have published authorities-disclosed research on AI-orchestrated attacks and autonomous APT frameworks. This is not academic knowledge applied to a new problem. It is practitioner expertise developed at the frontier of adversarial AI research.
Methodology
AI System Architecture Review
Model identification and version analysis, deployment configuration review, API and integration mapping, data flow analysis through the AI stack, and identification of trust boundaries between AI components and downstream systems.
Prompt Injection Testing
Direct prompt injection against system instructions, indirect prompt injection via external data sources (RAG pipelines, web browsing, document processing), multi-turn jailbreak chain construction, and context window manipulation.
Safety & Guardrail Assessment
Systematic evaluation of content filtering and safety alignment controls. Testing across known and novel bypass techniques. Assessment of whether safety controls are robust against adaptive adversarial strategies or brittle against minor reformulations.
Agentic Pipeline Testing
Tool call abuse and unauthorized action induction, goal hijacking through environmental manipulation, memory poisoning in persistent agent architectures, cross-agent trust exploitation in multi-agent systems, and privilege escalation through agent-to-agent communication.
Model Extraction & Inference Attacks
System prompt extraction, model fingerprinting and version identification, training data membership inference, and model inversion attempts against fine-tuned deployments.
ML Infrastructure & Supply Chain
Training pipeline integrity, dataset poisoning surface analysis, model registry access control, MLflow and model management platform security, dependency and supply chain risk assessment, and CI/CD pipeline security for model deployment workflows.
AI-Integrated Application Testing
Security assessment of the surrounding application infrastructure: API authentication and authorization, rate limiting and abuse prevention, output handling and injection into downstream systems, and sensitive data leakage through model responses.
What We Test
- LLM-integrated web and enterprise applications
- Custom fine-tuned model deployments
- RAG (Retrieval-Augmented Generation) pipelines
- Autonomous AI agents with tool access
- Multi-agent orchestration systems (LangChain, AutoGen, CrewAI, custom)
- AI-powered security tooling (SIEM co-pilots, SOC automation, threat intel platforms)
- MLSecOps pipelines and model training infrastructure
- Model serving infrastructure (API endpoints, inference servers)
- GenAI document and data processing workflows
Compliance Alignment
| Framework | Requirement |
|---|---|
| OWASP Top 10 for LLM Applications | Full coverage across all 10 categories |
| OWASP ML Security Top 10 | ML-specific vulnerability coverage |
| MITRE ATLAS | Adversarial machine learning threat matrix coverage |
| ISO 42001:2023 | AI management system security requirements |
| EU AI Act | High-risk AI system security testing obligations |
| NIST AI RMF | AI risk management framework — GOVERN, MAP, MEASURE, MANAGE |
Deliverables
- AI Attack Surface Map — complete inventory of AI components, data flows, trust boundaries, and integration points
- Prompt Injection Evidence Report — documented injection chains with reproduction steps and impact assessment
- Agentic Pipeline Risk Assessment — tool abuse scenarios, goal hijacking paths, and privilege escalation findings
- OWASP LLM Top 10 Coverage Report — test coverage and findings mapped to each OWASP LLM category
- MITRE ATLAS Mapping — findings mapped to adversarial ML tactic and technique identifiers
- Executive Summary — AI security risk narrative for leadership and board audiences
- Technical Report — full evidence, reproduction steps, and remediation guidance
- Retest Window — post-remediation verification
Ready to scope this engagement?
Tell us about your environment, regulatory drivers, and timeline. We will align methodology, scope, and evidence requirements before testing begins.