Macro-Level System Red Teaming

• Macro-level system red teaming is an interdisciplinary approach that evaluates and mitigates systemic vulnerabilities in complex AI systems throughout their entire lifecycle.
• It combines technical analysis with sociotechnical factors by using simulation, threat modeling, and multi-agent testing to uncover emergent risks.
• This framework drives continuous AI safety improvements through iterative testing, coordinated disclosure, and dynamic risk assessment.

Macro-level system red teaming is an interdisciplinary framework and practice for uncovering, evaluating, and remediating vulnerabilities in complex AI systems—especially those built on LLMs—by systematically probing systemic, emergent, and sociotechnical risks throughout the lifecycle of development, deployment, and operation. This approach goes beyond the traditional focus on individual model outputs to encompass interactions among multiple technical components, human stakeholders, dynamic environments, and long-term operational behaviors (Majumdar et al., 7 Jul 2025).

1. Conceptual Foundations and Scope

Macro-level system red teaming is distinguished by its holistic scope, examining the full AI system context rather than isolated model-level flaws. It incorporates the entire AI lifecycle—including inception, design, data management, development, integration with external tools, deployment, maintenance, and retirement—explicitly addressing not just failures of models, but emergent behaviors, systemic vulnerabilities, and sociotechnical interactions that can arise from complex system dynamics (Majumdar et al., 7 Jul 2025, Wang et al., 30 May 2025).

The process is iterative and multidisciplinary. Red teams critically interrogate the problem framing (challenging the necessity of automation and AI), scrutinize architectural and interface choices, evaluate the full data lifecycle for quality and bias, monitor code and supply chains for adversarial risks, stress-test deployed infrastructure and interfaces, and continuously monitor longitudinal system behavior for degradation, drift, or novel risk emergence.

A core tenet is that vulnerabilities are not confined to the model boundaries; they often result from interactions across diverse system layers—technical subsystems, user behavior patterns, UI/UX decisions, monitoring infrastructure, and real-world feedback loops (Wang et al., 30 May 2025, Majumdar et al., 7 Jul 2025).

2. Methodologies and Technical Instrumentation

Macro-level system red teaming employs an array of methodologies that are adapted or extended from cybersecurity, adversarial machine learning, and safety engineering:

• System Decomposition: Red teams map technical (models, APIs, integration points, data pipelines) and nontechnical (governance, human factors) components, as visualized in annotated system diagrams (Majumdar et al., 7 Jul 2025).
• Realistic Threat Modeling: Emphasis is placed on adversarial scenarios that replicate real-world attacker access and intent, going beyond idealized $\ell_p$-norm constraints common in adversarial ML. Threat models address black-box, white-box, and hybrid access, multi-agent interactions, role-specific attacks, and long-horizon behaviors (Wang et al., 30 May 2025, He et al., 20 Feb 2025).
• Simulation of Deployment Context: System-level red teaming constructs digital sandboxes to mimic complete operational environments, allowing the red team to test interactions such as tool use, UI workflows, multi-agent communication structures (e.g., AutoGen, Camel), and simulated user/adversary behaviors over time (Wang et al., 30 May 2025, He et al., 20 Feb 2025).
• Trajectory and User Monitoring: Recognition that many vulnerabilities emerge over extended multi-turn dialogues or through user trajectories. Monitoring tools, often driven by LLM-based classifiers or log analysis, track and flag anomalous or exploitative behaviors, coupled with real-time or asynchronous alerting (Wang et al., 30 May 2025).
• Adversarial Orchestration and Multi-Agent Test Harnesses: Autonomous frameworks such as AutoRedTeamer (Zhou et al., 20 Mar 2025), RedAgent (Xu et al., 23 Jul 2024), CoP (Xiong et al., 1 Jun 2025), and RedRFT (Zheng et al., 4 Jun 2025) enable dual-agent or multi-agent attack workflows, memory-guided strategy selection, iterative adversarial adaptation, and scalability in the space of vulnerability exploration.
• Standardized Evaluation and Coordination: The system typically includes coordinated disclosure pipelines, standardized reporting templates, and iterative, layered integration with broader Test, Evaluation, Verification, and Validation (TEVV) processes (Majumdar et al., 7 Jul 2025, Ahmad et al., 24 Jan 2025).

Notably, system-level red teaming also comprises "red teaming the monitor"—testing the resilience of supervisory and monitoring layers against bypass and deception attempts (Wang et al., 30 May 2025).

3. Key Technical Advances and Empirical Results

Recent work has established sophisticated technical instrumentation for macro-level red teaming:

• Iterative and Agentic Approaches: Multi-round frameworks (e.g., MART (Ge et al., 2023), AutoRedTeamer (Zhou et al., 20 Mar 2025)) automate the interplay between adversarial agent(s) and the system, continuously iterating on attack generation and defense adaptation to close emergent vulnerabilities.
• Contextual and Strategy-Driven Attacks: Systems such as RedAgent (Xu et al., 23 Jul 2024) and CoP (Xiong et al., 1 Jun 2025) leverage context-aware profiling, memory-guided strategy selection, and composition of human-supplied principles to design more adaptive, efficient, and diverse attack campaigns.
• Fine-Grained Coverage and Taxonomy-Based Generation: HARM (Zhang et al., 25 Sep 2024) demonstrates comprehensive top-down test case generation using extensible, multi-axis risk taxonomies, outputting large-scale, diverse test suites (e.g., over 128,000 unique cases) to cover long-tail risks.
• Multi-Turn and Multi-Agent Vulnerability Discovery: Multi-turn adversarial probing (HARM, RedRFT (Zheng et al., 4 Jun 2025)), multi-agent system communication attacks (Agent-in-the-Middle (He et al., 20 Feb 2025)), and memory-guided learning cycles significantly increase the breadth and depth of revealed vulnerabilities.
• Quantitative Scaling Laws: The effectiveness of attacks is shown to correlate with the "capability gap" between attacker and target models, following a predictable scaling law (ASR as a sigmoid function of capability difference) (Panfilov et al., 26 May 2025). This provides a predictive, quantitative framework for risk assessment.

Empirical studies consistently reveal that more sophisticated, context-aware, and multi-stage red teaming systems greatly outpace manual and template-driven approaches in both efficiency and coverage, e.g. reducing violation rates by 84.7% after four MART rounds (Ge et al., 2023), achieving over 90% success with <5 queries for RedAgent (Xu et al., 23 Jul 2024), and matching human-curated test suite diversity with AutoRedTeamer (Zhou et al., 20 Mar 2025).

4. Sociotechnical and Organizational Dimensions

System-level red teaming explicitly acknowledges that technical vulnerabilities are inseparable from sociotechnical substrate and organizational practices:

• Values and Perception of Harm: Determinations of "harm" are value-laden and context-dependent. The choice of risk categories, prioritization of safety specifications, and alignment with societal norms are not exclusively technical decisions (Gillespie et al., 12 Dec 2024).
• Labor and Well-Being: Red teaming involves diverse labor arrangements (internal, external, crowdsourced), with associated challenges relating to worker protections, psychological impacts, and accountability, paralleling issues in large-scale content moderation (Gillespie et al., 12 Dec 2024).
• Multifunctional Teams: Effective macro-level red teaming depends on cross-disciplinary teams that blend technical, policy, ethical, legal, and domain expertise. This structure enables detection of system-level, emergent, and second-order risks beyond the purview of specialized technical audits (Majumdar et al., 7 Jul 2025).

These perspectives motivate calls for enhanced transparency, diversity, and continuous empirical paper of red-team practices themselves (Gillespie et al., 12 Dec 2024, Majumdar et al., 7 Jul 2025).

5. Integration with Automated Evaluation, Monitoring, and Remediation

Macro-level system red teaming is designed to augment and seed automated evaluation pipelines:

• Automated and Reproducible Testing: Data from manual or agentic red teaming campaigns are scaled via automated test case generation (often using advanced LLMs) and integrated into continuous evaluation systems that monitor deployed models and applications for recurrent or newly emerging risks (Ahmad et al., 24 Jan 2025, Munoz et al., 1 Oct 2024).
• Trajectory Analysis and User-Level Monitoring: Aggregation and anomaly detection at session or user trajectory granularity (beyond single-query analysis) enable recognition and real-time mitigation of slow-burn or cumulative risks (Wang et al., 30 May 2025).
• Rapid Response and Mitigation: Emphasis is placed on responsive safeguard updates and patching once vulnerabilities are identified, with a process for continuous improvement and threat landscape adaptation (Wang et al., 30 May 2025).

These approaches are designed to be resilient to fast-changing threats and enable robust operational assurance.

6. Distinction Between Macro-Level and Micro-Level Red Teaming

Macro-level system red teaming is explicitly contrasted with micro-level model red teaming:

Dimension	Macro-Level Red Teaming	Micro-Level Model Red Teaming
Scope	Full system lifecycle and operational context	Isolated model behaviors
Focus	Emergent/systemic vulnerabilities; TEVV; organizational processes	Prompt-based model flaws, edge cases, individual biases
Team Structure	Cross-disciplinary/multifunctional	Primarily technical
Timeline	Holistic: Inception to Retirement	Development and alignment
Attack Methods	Life-cycle interventions, agentic, multi-agent, role-based, trajectory, context- and environment-aware	Prompt engineering, single- or multi-turn adversarial inputs
Evaluation	System risk, second-order effects, coordinated disclosure	Attack success rate, robustness, specific output metrics

Macro-level efforts provide context for model-level results, ensuring that isolated technical findings are interpreted and remediated within the complex realities of sociotechnical deployment (Majumdar et al., 7 Jul 2025, Zhang et al., 25 Sep 2024, Ge et al., 2023).

7. Challenges, Controversies, and Future Directions

Critical challenges for macro-level red teaming include:

• Resource Intensity and Scalability: Running comprehensive system-level red team campaigns is often resource-intensive, requiring substantial financial and human investments, and may not scale uniformly across organizations (Ahmad et al., 24 Jan 2025).
• Emergent Behavior and Second-Order Risks: Many system-level risks only manifest when components interact or scale, requiring forward-looking methodologies to capture second-order and societal impacts.
• Changing Threat Landscape: The rise of highly capable, open-source attacker models increases risks to deployed systems. The predictive scaling laws indicate that fixed-capability human red teams will become insufficient as system capabilities rise (Panfilov et al., 26 May 2025).
• Integration of Technical and Social Evaluation: Red teaming must continue evolving to consider both technical and social dimensions, combining agentic automation with human insight, and integrating insights back into model and system design (Majumdar et al., 7 Jul 2025, Gillespie et al., 12 Dec 2024).
• Ethical and Transparency Considerations: The balance between disclosure of vulnerabilities and prevention of information hazards is delicate. Transparency, diversity, and accountability in red team composition and process are necessary safeguards (Gillespie et al., 12 Dec 2024, Ahmad et al., 24 Jan 2025).

Future work is focused on formalizing methodologies for system-level threat modeling, expanding the use of composable benchmarks (Zheng et al., 4 Jun 2025), integrating red teaming findings directly into safety assurance pipelines, and fostering a culture of continuous, coordinated, and multidisciplinary evaluation (Majumdar et al., 7 Jul 2025, Munoz et al., 1 Oct 2024, Wang et al., 30 May 2025).

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Macro-level system red teaming thus emerges as an essential, holistic discipline at the intersection of safety engineering, adversarial risk assessment, and sociotechnical systems analysis. Its systematic, lifecycle-focused practices are foundational for assuring trustworthy operation of increasingly agentic, interconnected, and high-stakes AI systems.