Red AI: Adversarial Testing of AI/ML Systems

• Red AI is a framework for adversarial testing of AI/ML systems that uses both human red teaming and automated agent-based attacks.
• It employs techniques like black-box testing, prompt mutation, and persona-driven simulations to quantify vulnerabilities and efficiency gains.
• Insights from Red AI inform cybersecurity practices through standardized benchmarking, STIX-compliant threat intelligence, and continuous risk management.

“Red AI” encompasses the systematic, adversarial probing and exploitation of AI and ML models using attacker-style methodologies. Red AI spans manual red-teaming by human experts, automated agent-based attacks, and the integration of AI techniques themselves in generating cyberattacks. The field is defined by its focus on surfacing vulnerabilities, quantifying model robustness, and catalyzing remediation via knowledge-sharing pipelines. Red AI functions as the offensive analog of classical cybersecurity red teams, and is now a cornerstone of AI safety engineering, model evaluation, and lifecycle risk management (Nguyen et al., 2022, Ahmad et al., 24 Jan 2025, Dawson et al., 17 Jun 2025, Al-Azzawi et al., 25 Mar 2025).

1. Foundational Concepts and Scope

Red AI refers to the deliberate adversarial testing of AI/ML systems—by human or autonomous AI agents—to uncover flaws, characterize vulnerabilities, and enable targeted defenses. This practice extends to black-box and white-box attacks, model-agnostic fuzzing, and realistic scenario-driven adversarial campaigns (Nguyen et al., 2022, Ahmad et al., 24 Jan 2025). Motivating use cases include:

• Discovery of training-time and runtime weaknesses, including poisoning, evasion, and extraction attacks.
• Quantification of robustness, including relative accuracy drops under attack, and the measurement of model resilience.
• Characterization of adversary TTPs (Tactics, Techniques, Procedures) for cataloging and defense design.
• Knowledge-sharing of threats and vulnerabilities across researchers, developers, and operators.

Red AI methodologies now underpin standardized frameworks (e.g., MITRE ATLAS) and systematized benchmarking regimes (Dawson et al., 17 Jun 2025).

2. Principal Methodologies and Benchmarking Approaches

Red AI is implemented through both manual and automated means. Manual (human-in-the-loop) external red teaming emphasizes domain expertise, creativity, and contextual judgment beyond what automated pipelines offer (Ahmad et al., 24 Jan 2025). Automating Red AI leverages LLM agents, evolutionary prompt mutation, and agent-based adversarial simulation.

In systematized AI red teaming, as exemplified by the CTI4AI prototype, adversarial attacks are executed in a controlled pipeline using standard tools (e.g., IBM ART). For example, vulnerability is quantified using the Fast Gradient Method: $$x_{\mathrm{adv}} = x + \varepsilon \,\operatorname{sign}\!\bigl(\nabla_{x} J(\theta, x, y)\bigr)$$ with $x$ as clean input, $y$ as the true label, $J$ the loss, and $\varepsilon$ the perturbation magnitude. Vulnerability is further measured as: $$\Delta_{\mathrm{acc}} = \mathrm{Acc}_{\mathrm{clean}} - \mathrm{Acc}_{\mathrm{adv}}$$

$$V = \frac{\Delta_{\mathrm{acc}}}{\mathrm{Acc}_{\mathrm{clean}}}$$

Higher $V$ indicates more severe vulnerability (Nguyen et al., 2022).

Automated, agent-based Red AI is exemplified by AIRTBench, which evaluates LLMs in black-box CTF environments using code-writing agents to compromise AI systems (Dawson et al., 17 Jun 2025). Evaluation metrics involve Suite Success Rate (SSR) and Overall Success Rate (OSR), capturing both breadth and depth of exploitation capabilities.

3. Architectures, Taxonomies, and Data Structures

Red AI workflows are now encoded in modular system architectures. CTI4AI, for instance, is divided into:

• Red-Team Generator: Executes adversarial attacks against target models, generating raw vulnerability data.
• Threat Intelligence Encoder: Maps artifacts into STIX-compliant, AI-extended taxonomies (AITI), capturing attack patterns, affected personas, and use cases.
• Threat Intelligence Sharing: Disseminates structured reports via TAXII/MISP APIs, enabling real-time intelligence updates (Nguyen et al., 2022).

Taxonomies and data schemas are grounded in STIX v2.x, with domain-specific extensions for AI. Each vulnerability is richly annotated (attack method, sophistication, persona affected, etc.) to enable downstream integration and analysis.

Benchmarking methodologies, such as AIRTBench, enforce standardized interaction environments (e.g., Docker-based Jupyter harnesses) and fixed metrics for composable, reproducible evaluation (Dawson et al., 17 Jun 2025). PersonaTeaming introduces structured persona mutation, proving that the background and identity of red-teamers/agents impacts both the spectrum and potency of attacks; empirical results show up to 144.1% improvement in attack success rate over baseline prompt-mutation frameworks (Deng et al., 3 Sep 2025).

4. Domains and Applications: From Cybersecurity to Clinical Red Teaming

Red AI is operationalized across diverse domains:

• Adversarial ML Evaluation: Systematic attacks (e.g., FGM, PGD, poisoning) against classification or generative models.
• Cybersecurity Red Teaming: Offensive use of neural networks (DNNs, LSTMs), GANs, and RL for phishing, password cracking, data exfiltration, and zero-day discovery (Al-Azzawi et al., 25 Mar 2025).
• AI-Driven Red Teaming of LLMs: Automated CTF frameworks where advanced LLMs discover and chain exploits. State-of-the-art LLMs (Claude-3.7-Sonnet, Gemini-2.5-Pro) solve over half of realistic AI/ML vulnerability challenges far faster than human security researchers (efficiency gains exceeding 5,000× on hard tasks) (Dawson et al., 17 Jun 2025).
• Socio-Behavioral Risk Identification: PersonaTeaming algorithmically broadens risk coverage by introducing “red-teaming expert” and “regular user” personas, systematically sampling attacker perspectives (Deng et al., 3 Sep 2025).
• Simulation-based Clinical AI Red Teaming: Scalable, longitudinal evaluation of LLMs in high-risk deployments (e.g., mental health therapy), using simulated patient agents and automated crisis response detection (Steenstra et al., 23 Feb 2026).

5. Technical Implementations and Experimental Results

Technical implementations leverage adversarial pipelines, automated batch jobs, and both statistical and embedding-based diversity metrics. Notable workflow components include:

• Automated or semi-automated vulnerability generation, metric collection, and threat labeling.
• Use of LLMs in chained adversarial, evaluation, and persona-generation roles.
• Embedding-based “mutation distance” metrics to quantify the semantic novelty and span of adversarial prompts beyond lexical changes (Deng et al., 3 Sep 2025).
• Interactive dashboards for red-team auditing, visualization of risk trajectories, and equity analyses in red-teamed clinical simulations (Steenstra et al., 23 Feb 2026).

Empirical results demonstrate dramatic degradation of unprotected models under attack (e.g., ResNet-50 on CIFAR-10: 90.74% to 44.41% post-FGM, $V\approx0.51$), high attack success rates with persona-adaptive mutation (up to 0.28 ASR for expert personas), and the surfacing of subtle, longitudinal harms (“AI psychosis,” co-rumination, crisis protocol non-adherence) not detectable by one-shot adversarial prompts.

6. Limitations, Strengths, and Future Directions

Red AI’s primary strengths include increased attack discovery efficiency, broader risk coverage, and systematized sharing of vulnerability intelligence. Automated approaches (AIRTBench, PersonaTeaming) yield efficiency gains (up to 5,000× fold) and broader threat space exploration vs. human-only techniques.

Limitations persist: high-quality Red AI is data-hungry, risks rapid obsolescence as models and defenses evolve, and may unintentionally empower malicious actors if mitigation is not coordinated with responsible disclosure. Persona-based prompt mutation, while increasing diversity, may oversimplify real-world attacker identities and requires human validation for subjective or high-stakes harms. Clinical red teaming frameworks highlight the inadequacy of current LLMs in crisis escalation and the necessity of human-in-the-loop guardrails prior to deployment (Steenstra et al., 23 Feb 2026).

Future research integrates continuous and hybrid red teaming into CI/CD pipelines, expands persona coverage, and formalizes standardized risk-scoring methodologies. Systematic recording and sharing (STIX-TAXII/AITI pipelines) are now recommended best practices for closing the discovery-to-mitigation loop (Nguyen et al., 2022).

7. Comparative Overview of Red AI Approaches

Approach/Tool	Domain	Key Features
CTI4AI (Nguyen et al., 2022)	Adversarial ML, threat intelligence	Automated attack pipeline, STIX/AITI encoding, TAXII sharing
AIRTBench (Dawson et al., 17 Jun 2025)	LLM security	Black-box CTFs, SSR/OSR metrics, multi-category coverage
PersonaTeaming (Deng et al., 3 Sep 2025)	Automated adversarial prompt generation	Persona-driven mutation, diversity/embedding metrics
Clinical Red Teaming (Steenstra et al., 23 Feb 2026)	Mental health LLMs	Longitudinal patient simulation, crisis detection, interactive dash
Al-Azzawi et al. (Al-Azzawi et al., 25 Mar 2025)	Cybersecurity	AI-based attack generation (DNN/LSTM/GAN/RL), kill-chain analysis
OpenAI External Red Teaming (Ahmad et al., 24 Jan 2025)	General LLM safety	Expert-driven, multi-domain, scenario-guided risk surfacing

This table offers a domain-specific overview, highlighting the diversity of Red AI methodologies across safety, benchmarking, and offensive cybersecurity applications.

• (Nguyen et al., 2022) CTI4AI: Threat Intelligence Generation and Sharing after Red Teaming AI Models
• (Al-Azzawi et al., 25 Mar 2025) Red Teaming with Artificial Intelligence-Driven Cyberattacks: A Scoping Review
• (Ahmad et al., 24 Jan 2025) OpenAI's Approach to External Red Teaming for AI Models and Systems
• (Dawson et al., 17 Jun 2025) AIRTBench: Measuring Autonomous AI Red Teaming Capabilities in LLMs
• (Deng et al., 3 Sep 2025) PersonaTeaming: Exploring How Introducing Personas Can Improve Automated AI Red-Teaming
• (Steenstra et al., 23 Feb 2026) Assessing Risks of LLMs in Mental Health Support: A Framework for Automated Clinical AI Red Teaming