AI Signal 450
Presentation: Leveraging Adversary Emulation for GenAI Red Teaming
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A presentation showed how to use adversary emulation and the MITRE ATLAS framework to red-team GenAI applications on AWS.
Red-team testing reveals weaknesses in LLMs and knowledge bases that standard cloud security checks miss, such as data poisoning and LLMjacking. Mapping those tests to MITRE ATLAS gives engineers a repeatable method to build guardrails and satisfy emerging compliance demands. The approach pushes security teams to adopt an attacker mindset, which can improve resilience of production AI services.
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Adversary emulation is presented as a practical way to probe GenAI models for attacks like data poisoning and LLMjacking.
The method ties red-team activities to the MITRE ATLAS framework, creating systematic guardrails for AWS-hosted AI workloads.
Security teams are urged to develop new skills and processes to shift from confidence-based defense to attacker-simulated testing.
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The presentation introduces a shift from traditional cloud security to an adversary-emulation mindset for generative AI, specifically targeting large language models and associated knowledge bases. By aligning red-team exercises with the MITRE ATLAS knowledge base, engineers gain a structured taxonomy for potential AI-specific threats. This alignment is positioned as a bridge between existing cloud security practices and emerging AI risk frameworks.
Adopting this approach requires allocating red-team or purple-team resources to design and execute simulated attacks, as well as training engineers to interpret MITRE ATLAS techniques in the context of AI pipelines. Organizations may need to integrate Mitigant-provided tooling or develop in-house scripts that interact with AWS services to conduct the emulation. The upfront effort includes mapping current AI workloads to ATLAS tactics and establishing guardrails based on the findings.
The guidance is demonstrated within an AWS environment, so the specific tooling, permissions, and attack vectors described may not directly translate to other cloud providers or on-premise deployments. Additionally, the focus is on a subset of known attacks, data poisoning and LLMjacking, leaving other emerging threats outside the immediate scope. Teams should treat the presented techniques as a starting point rather than a comprehensive security solution.
Compliance considerations are highlighted, noting that proactive red-team testing can help satisfy regulations such as the EU AI Act and ISO standards that demand robust AI safeguards. By exposing data leakage and malicious content generation risks early, organizations can avoid costly breaches and reputational damage. The presentation therefore frames adversary emulation as both a security and a compliance enabler.
Practically, engineers can begin by cataloguing their GenAI components, selecting relevant MITRE ATLAS techniques, and running controlled adversary simulations in a test environment. Findings should be fed back into CI/CD pipelines to enforce guardrails automatically. Continuous iteration of these tests will keep the AI stack resilient as models and threat landscapes evolve.
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