AI Red Teaming
Adversarial Testing for the Systems Powering Your Organization in 2026.
Your AI systems have been tested for performance, for accuracy, and for hallucination rate. They have not been tested by an adversary who understands how to manipulate them at the instruction level, exploit the trust boundaries between their components, and use them against your organization's interests.
Evaluris AI Red Teaming is a dedicated adversarial assessment practice for LLM-powered applications, autonomous AI agents, multi-agent systems, and AI-integrated enterprise infrastructure. Our operators have published original research on system prompt poisoning, autonomous APT frameworks, and agentic attack orchestration — not theoretical familiarity with these risks, but active practitioner knowledge applied to every engagement.
Why AI Red Teaming Is a Distinct Practice
Conventional red team methodology does not transfer to AI systems. The attack surface is fundamentally different. The failure modes are unlike anything in the traditional security taxonomy. And the consequences of a successful attack against an AI system can be more severe than a conventional compromise — because AI systems are often trusted, integrated into critical business processes, and given access to internal data and APIs that conventional attackers could not reach directly.
A prompt injection that extracts your system prompt and customer data is not a web application vulnerability. An autonomous agent that is manipulated into executing unauthorized transactions is not a privilege escalation finding. These are new classes of risk that require a new class of assessment.
Evaluris has published authorities-disclosed research on AI-orchestrated attacks across borders. We understand what adversarial AI looks like from the operator side — and we use that understanding to test whether your AI systems are resistant to it.
Methodology
AI System Threat Modeling
Identification of all AI components in scope, data flows, trust boundaries, tool integrations, and privilege levels. Adversary modeling specific to the AI system type — what an attacker with knowledge of LLM behavior would attempt against this specific deployment.
Prompt Injection Campaign
Systematic direct and indirect prompt injection testing across all input surfaces: user prompts, system prompt override attempts, document processing inputs (PDF, Word, web content), RAG pipeline data source injection, and third-party tool response manipulation.
Safety Boundary Assessment
Evaluation of guardrails, content filtering, and safety alignment controls against adaptive adversarial strategies. Testing includes known jailbreak techniques and novel approaches developed from first-principles analysis of the specific model's alignment characteristics.
Agentic System Adversarial Testing
For systems with autonomous agent capabilities: goal hijacking through environmental manipulation, unauthorized tool invocation chains, cross-agent trust exploitation in multi-agent architectures, memory poisoning in persistent agent systems, and privilege escalation through agent-to-agent communication patterns.
Data Extraction & Exfiltration
System prompt extraction, training data inference, sensitive information extraction through conversational manipulation, and cross-user data leakage in multi-tenant AI deployments.
Integration & Application Layer
Security testing of the surrounding application: API authentication abuse, rate limiting bypass, output injection into downstream systems, excessive agency exploitation, and insecure plugin or function call configurations.
What We Test
- Customer-facing LLM chatbots and support systems
- Internal AI assistants with access to enterprise data
- AI-powered security tooling (SIEM co-pilots, threat intelligence platforms, automated response systems)
- Autonomous agents with API and tool access
- RAG-based knowledge management and document analysis systems
- Multi-agent orchestration platforms
- AI code generation tools in developer workflows
- Fine-tuned model deployments on proprietary data
- AI-powered decision systems in financial services, HR, and compliance
Compliance Alignment
| Framework | Requirement |
|---|---|
| OWASP Top 10 for LLM Applications | Full adversarial coverage across all 10 categories |
| MITRE ATLAS | Adversarial machine learning threat matrix — full technique coverage |
| ISO 42001:2023 | AI management system security and risk requirements |
| EU AI Act | High-risk AI system adversarial robustness testing |
| NIST AI RMF | MEASURE and MANAGE functions — adversarial robustness assessment |
| OWASP ML Security Top 10 | ML-specific vulnerability coverage |
Deliverables
- AI Threat Model — complete attack surface map of AI components, trust boundaries, and adversary scenarios
- Prompt Injection Evidence Report — documented injection chains with full reproduction steps and impact assessment
- Agentic Exploitation Report — tool abuse chains, goal hijacking scenarios, and cross-agent findings
- Safety Control Assessment — guardrail effectiveness rating with bypass evidence and improvement recommendations
- OWASP LLM Top 10 Coverage Matrix — test coverage and findings per category
- MITRE ATLAS Mapping — all 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.