AI-Driven Red Teaming

• AI-driven red teaming is a systematic approach that uses both automated and human-AI methods to expose vulnerabilities in AI systems.
• It applies frameworks like RED-AI, APRT, and ASTRA to identify adversarial attacks, data poisoning, and emergent behavioral failures.
• The practice emphasizes continuous evaluation with metrics such as AER and ASR while addressing sociotechnical challenges for improved security.

AI-driven red teaming refers to the systematic use of artificial intelligence tools, models, and methodologies to probe, test, and expose vulnerabilities in AI-powered systems. This practice applies adversarial testing—originating from cybersecurity and systems engineering—to the specific, often opaque vulnerabilities and emergent behaviors inherent to AI, with the aim of improving security, safety, and trustworthiness across a variety of domains such as LLMs, autonomous systems, healthcare, and cryptographic standards. Recent research highlights both automated and hybrid (human-AI) approaches, expanding red teaming beyond traditional penetration to include sociotechnical, lifecycle, and regulatory dimensions.

1. Conceptual Foundations and Scope

AI-driven red teaming has evolved from the adversary simulation and penetration-testing traditions in cybersecurity, but adapts to the unique risks, failure modes, and emergent properties of AI systems (Sinha et al., 14 Sep 2025). Unlike conventional software, AI models exhibit inherent vulnerabilities such as adversarial susceptibility, input distribution drift, a vulnerability to data poisoning, and unpatchable behaviors. Consequently, AI red teaming addresses both technical and socio-technical failure surfaces—ranging from adversarial attacks (e.g., model evasion, backdoor triggers) to value-laden outputs (“sociotechnical harms” such as bias or the hallucination of sensitive data) (Gillespie et al., 12 Dec 2024).

Table: Evolution of Red Teaming Paradigms

Domain	Red Teaming Focus	Distinct AI Vulnerabilities
Traditional Cyber	Exploiting software vulnerabilities; pen-testing	Input validation, protocol flaws
AI/ML-Driven Systems	Model-level adversarial attacks; emergent behaviors	Adversarial examples, data/model extraction, unpatchable bugs

AI-driven red teaming further extends to the full lifecycle and deployment context of AI systems, including systems-of-systems and mission-critical applications such as maritime autonomous platforms (Walter et al., 2023). The “big-tent” definition (Feffer et al., 29 Jan 2024) covers any structured adversarial process—manual or automated—designed to systematically expose, and thereby mitigate, AI-specific risks.

2. Methodological Frameworks and Approaches

Methodologies in AI-driven red teaming now span structured checklist-driven frameworks, formal threat modeling, hybrid human-AI pipelines, and fully autonomous adversarial agent systems. Notable frameworks include:

• RED-AI: A comprehensive, checklist-based process for scoping, threat modeling, adversarial evaluation (white-box and black-box), and post-evaluation reporting, with explicit mathematical formulations for attack classes (e.g., poisoning, patch, evasion) (Walter et al., 2023).
• Automated Progressive Red Teaming (APRT): An iterative agent framework with three modules—Intention Expanding LLM, Intention Hiding LLM, and the Evil Maker—interacting in multi-round adversarial loops. It introduces the Attack Effectiveness Rate (AER) as an evaluation metric and is shown to elicit unsafe responses in leading LLMs at rates up to 54% for Llama-3-8B-Instruct (Jiang et al., 4 Jul 2024).
• AutoRedTeamer: A fully automated, dual-agent, memory-driven system integrating attack selection and discovery of new vectors from academic literature. It achieves 20% higher attack success rates and 46% lower computational costs in evaluation compared to baselines (Zhou et al., 20 Mar 2025).
• ASTRA: A spatial-temporal agentic system relying on domain-specific knowledge graphs, Monte Carlo sampling of input spaces, and chain-of-thought interrogation, achieving up to 66% more effective vulnerability discovery than static adversarial benchmarks (Xu et al., 5 Aug 2025).
• AIRTBench: Black-box evaluation of autonomous red teaming capabilities in LLMs via “capture the flag” challenges that require code generation to exploit AI/ML infrastructure. Frontier models demonstrate an overall success rate of up to 46.9%, with pronounced efficiency advantages over human security researchers (Dawson et al., 17 Jun 2025).
• PersonaTeaming: A dynamic prompt-mutation approach that incorporates persona (both adversarial expert and regular user) adaptation, achieving up to 144.1% increased attack success rates and new diversity metrics (e.g., mutation distance in embedding space) (Deng et al., 3 Sep 2025).

Many frameworks advocate tailoring the exercise to the specific system, deployment scenario, and threat model, and emphasize continuous (not one-off) evaluation as threat landscapes evolve (Walter et al., 2023, Feffer et al., 29 Jan 2024, Zhou et al., 20 Mar 2025).

3. Technical Attack Taxonomies and Implementation Details

AI-driven red teaming draws from and extends a diverse toolkit of attack vectors. Examples include:

• Data poisoning: Injected during training as $(x_i^*, y_i^*)$, optimized to maximize loss on targeted tasks:

$$\max_{\{x_i^*, y_i^*\}_{i=1}^{N}} L(\theta^*, \{x_i^*, y_i^*\}), \quad \theta^* = \arg\min_{\theta} L(\theta, \{\text{normal}\} \cup \{\text{poison}\})$$

• Adversarial evasion: Input perturbation, e.g. via Fast Gradient Sign Method (FGSM):

$$X' = X - \epsilon \cdot \operatorname{sign}(\nabla_x J(\theta, X, y_{\text{target}}))$$

• Adversarial patches: Optimizing over space of physical input patches:

$$\arg\max_{P} \mathbb{E}_{x,t,l} [\log \Pr(\hat{y} | A(P, x, l, t))]$$

• Prompt injection and model extraction (LLMs): Sequential dialogue-based attacks framed as long-horizon Markov Decision Processes, trained via hierarchical RL with token-level harm rewards (Belaire et al., 6 Aug 2025).
• Knowledge graph-augmented misinformation attacks: Multilingual, location-grounded generation of adversarial prompts for disinformation, utilizing knowledge graphs built from clustered real-world claims (Cuevas et al., 23 Sep 2025).

AI models used for attack generation include transformers, GANs, LSTMs, reinforcement learning agents, and evolutionary algorithms, while real-time anomaly detection in testing quantum-resistant cryptography leverages models such as Isolation Forests and PCA (Radanliev, 26 Sep 2025).

4. Automation, Human-AI Synergy, and Sociotechnical Challenges

Automation is increasingly central to scalable red teaming, enabling the generation and triage of large numbers of adversarial inputs, continuous monitoring, and autonomous discovery of emergent threat modes (Zhou et al., 20 Mar 2025, Jiang et al., 4 Jul 2024). Nonetheless, research cautions that automation must augment, not replace, human domain expertise, since contextual and value-laden failures, as well as nuanced context-dependent threats, are often only detectable through expert human judgment (Zhang et al., 28 Mar 2025, Gillespie et al., 12 Dec 2024).

Critical sociotechnical issues emerge, such as:

• Labor practices: Risks of worker precarity, overexposure to distressing content, and mental health harms paralleling those seen in content moderation. Individual and organizational mitigation strategies—e.g., de-roling, resilience training, peer support, and contract reform—are proposed to safeguard practitioner well-being (Pendse et al., 29 Apr 2025, Gillespie et al., 12 Dec 2024).
• Accountability and governance: Without transparency, adequate labor protections, and explicit value alignment, red teaming can devolve into “security theater,” with exercises serving as mere regulatory compliance rather than substantive risk reduction (Feffer et al., 29 Jan 2024, Gillespie et al., 12 Dec 2024). A risk function capturing the interplay of technical (V), labor (L), and ethical (E) factors is: $R = f(V, L, E)$.

A balanced model is advocated, for which the overall effectiveness $E$ depends on the degree of automation $A$, proficiency $P$, agency $G$, and adaptability $D$:

$E = A(P, G, D)$

5. Evaluation Practices and Metrics

AI-driven red teaming is characterized by rigorous, multi-dimensional evaluation frameworks:

• Attack effectiveness rate (AER): Measures the proportion of adversarial attempts that yield unsafe or jailbreak responses; normalized for attempt volume and validated against human judgment (Jiang et al., 4 Jul 2024).

$$\text{AER} = \frac{\sum_{i=1}^N \mathbb{I}(\text{unsafe}(p_i, d_i))}{N}$$

• Attack success rate (ASR): Fraction of test cases that induce detectable harm, implemented as:

$$\text{ASR} = \frac{1}{N} \sum_{i=1}^N \text{JUDGE}(\text{LLM}(p'_i))$$

• Mutation distance and diversity: Use of embedding-based (e.g., SentenceTransformer) L2 distances and Self-BLEU to quantify diversity and semantic drift among adversarial prompts (Deng et al., 3 Sep 2025).
• Lifecycle and system-level risk aggregation: Systemic approach aggregates vulnerability measures across lifecycle stages:

$$V_{\text{sys}} = \sum_{i=1}^{7} V_{\text{stage}(i)}$$

and risk within model-level red teaming across strategies:

$$R_{\text{model}} = \sum_{j=1}^{m}\sum_{k=1}^{n} r(j, k)$$

Comprehensive reporting, continuous retesting, and integration with development pipelines are emphasized, as is the development of clear, quantitative safety benchmarks aligned with product-level specifications (Wang et al., 30 May 2025, Ahmad et al., 24 Jan 2025).

6. Domain-Specific Extensions and Global Implications

AI-driven red teaming demonstrates extensive domain adaptation:

• Autonomous systems (e.g., maritime robotics): Requires physical and software-level adversarial testing, tailored scenario development, and simulation of supply chain attacks, with in situ and digital twin evaluation (Walter et al., 2023).
• Machine translation and speech systems: Human-in-the-loop edge-case elicitation, taxonomies of error, hybrid auto/manual critical error identification (e.g., BLASER, COMET), and recommendation of automated warning thresholds (Ropers et al., 29 Jan 2024).
• Cryptography: AI-augmented penetration testing, adversarial fuzzing for quantum-resistant algorithms, reinforcement learning-based probe generation, and iterative anomaly-driven refinement (Radanliev, 26 Sep 2025).
• Disinformation and cross-cultural robustness: Multilingual, narrative-driven adversarial prompt datasets highlight the necessity of red teaming built on real-world global misinformation rather than US/English-centric heuristics (Cuevas et al., 23 Sep 2025).

Global deployment of AI models underscores the need for red teaming practices, datasets, and mitigation strategies calibrated to diverse legal, cultural, and operational contexts.

7. Future Directions and Open Challenges

Emergent research directions include:

• Lifecycle-, system-, and scenario-level integration: A shift from single-turn, isolated model testing to system-of-systems, scenario-driven, and macro-level lifecycle red teaming, coordinated by multifunctional teams spanning technical, policy, and domain expertise (Majumdar et al., 7 Jul 2025).
• Automation-hybrid pipelines: Iterative, co-evolutionary red and blue teaming cycles using agentic frameworks (e.g., ASTRA), chaining of knowledge graphs, and real-time alignment fine-tuning.
• Formalization, interoperability, and standardization: Adoption of standardized question banks, reporting protocols, and integration with established cybersecurity practices (structured threat modeling, adversary emulation, rules of engagement) (Feffer et al., 29 Jan 2024, Sinha et al., 14 Sep 2025).
• Psychosocial and labor research: Institutionalization of safeguards for red teamers (mental health, job stability), and broader sociotechnical analysis of value assumptions and labor impacts.
• Metric evolution and interpretability: Continued innovation in quantitative diversity, harm, and interpretability metrics—balancing detection performance with actionable insights for model and product stakeholders.

A plausible implication is that the maturing AI-driven red teaming field will increasingly resemble a synergistic, interdisciplinary ecosystem, in which technical, social, and procedural advances converge to provide defendable safety guarantees across the deployment spectrum. Sustained research, labor investment, and policy coordination remain prerequisites for maintaining pace with evolving adversarial and sociotechnical landscapes.