Cyber Red Teams: Adversary Emulation & Automation

• Cyber red teaming is the practice of emulating adversary tactics under defined rules of engagement to systematically expose vulnerabilities and challenge security assumptions.
• It employs both manual penetration testing and automated frameworks like MITRE Caldera and Atomic Red Team to provide scalable, high-fidelity risk evaluations.
• Integrations with adversarial machine learning and AI techniques underscore its role in identifying emerging vulnerabilities and informing adaptive defense strategies.

A cyber red team is a group of specialists or an automated system that simulates adversary tactics, techniques, and procedures (TTPs) to challenge the security posture of an organization, system, or autonomous decision process. Cyber red teams operate under a structured rules-of-engagement (RoE) framework and employ a spectrum of offensive measures—from penetration testing to adversary emulation and advanced adversarial machine learning—to systematically identify operational vulnerabilities, stress assumptions, and inform defense improvements across real-world and simulated operational domains. Cyber red teaming is distinct from—but often coordinated with—blue (defensive) and purple (integrated/offensive-defensive) teams, and has rapidly evolved to address the growing complexity, automation, and AI-specific risks in modern threat environments (Sinha et al., 14 Sep 2025, Landauer et al., 28 Aug 2024, Al-Azzawi et al., 25 Mar 2025).

1. Historical Foundations and Key Principles

Cyber red teaming originated from military wargaming, where adversarial teams (“red”) tested the resilience and preparedness of “blue” defensive forces through structured scenarios. These principles—challenge assumptions, mimic real threats, identify emergent failure modes—were adapted into cybersecurity through “tiger teams” in the 1970s and later formalized red teams, evolving into penetration testing, advanced persistent threat (APT) emulation, and security posture assessment (Majumdar et al., 7 Jul 2025, Sinha et al., 14 Sep 2025).

The canonical workflow involves:

• Defining scope, objectives, and explicit RoEs
• Threat modeling to select realistic/priority adversaries
• Multi-stage attack execution (often following frameworks such as the Cyber Kill Chain or MITRE ATT&CK matrix)
• Systematic reporting, coordinated vulnerability disclosure (CVD), and defense hardening interventions

This operational discipline distinguishes cyber red teaming from opportunistic or unstructured vulnerability assessments.

2. Techniques, Toolchains, and Automation

Red teams employ a variety of tools (manual and automated) to replicate the behavior and progression of sophisticated adversaries. Toolchains and automation frameworks have become central to enabling continuous assessment, scaling up test coverage, and reducing the resource requirements for repeated, high-fidelity engagements (Landauer et al., 28 Aug 2024, Syed et al., 7 Jan 2025).

Tool	Features/Strengths	Limitations
MITRE Caldera	Modular attack chains, strong usability, automation	Requires agent deployment, not Windows-native
Metasploit	In-depth exploit modules, robust docs, active user base	Steeper learning curve, Ruby dependency
Atomic Red Team	Extensive YAML-marked test library, multi-OS	Some scripting skill required for customization
CybORG	RL agent gym for red/blue ops, dual simulation/emulation	Limited to defined scenarios, initial RL agent realism
HARMer	Automated full cyberattack pipeline (HARM modeling)	Focused on attack graph-based scenarios, less social engineering

Automation tools frequently make use of formal modeling (HARM, attack graphs), scenario configuration, replayable actions, and integration with RL/adversarial ML (see below). Open-source frameworks support repeatable and scalable testing crucial for modern security assurances.

3. Red Teaming in Adversarial Machine Learning and AI

The development of AI and autonomous systems introduces novel attack surfaces and unpatchable vulnerabilities, such as adversarial examples, model extraction, membership inference, and prompt injection (Sinha et al., 14 Sep 2025, Majumdar et al., 7 Jul 2025, Nguyen et al., 2022). Cyber red teams have extended their methodology beyond classical exploits to include:

• Simulation of adversarial attacks on ML models (e.g., FGM, PGD, GAN-based data poisoning)
• Integrated red-blue simulation in dual-fidelity environments for AI-enabled operations (CybORG)
• Automated discovery of model-level and emergence-level vulnerabilities via RL and game-theoretic approaches (Shah et al., 2018, Ma et al., 2023)

This requires the systematic adversarial evaluation of both model robustness and system-level safety under dynamic, interactive, and multi-agent adversarial regimes. For example, in “Two Can Play That Game,” the defender’s RL policy for alert inspection was tested against an adversarial RL alert-generation policy, revealing both robustness boundaries and previously undiscovered exploits via a double oracle approach (Shah et al., 2018).

AI-specific red teaming also necessitates new reporting and threat intelligence sharing formats (STIX/AITI in CTI4AI (Nguyen et al., 2022)), and integration with conventional cyber threat models.

4. Cognitive, Human, and Operational Factors

Human subjects remain central to most red team operations, especially in social engineering, decision modeling, and adversarial testing of AI models. Recent work (GAMBiT (Beltz et al., 28 Aug 2025)) demonstrates that cognitive biases (loss aversion, sunk cost fallacy, confirmation bias) can significantly deviate red team performance from rational-optimal strategies, influencing both attack paths and detection risk. High-fidelity cyber range experiments now bundle psychometric profiling, operational telemetry, and granular adversary interaction logs to fuel behavioral modeling.

Simultaneously, in AI red teaming, psychological health and occupational hazards for red team personnel are now a critical concern due to regular exposure to harmful or morally injurious content and the need for “interactional labor” (simulating offensive personas) (Pendse et al., 29 Apr 2025, Zhang et al., 10 Jul 2024). Organizational and technical strategies—including structured debriefing, peer support, and human-in-the-loop controls—are being proposed to mitigate these risks and sustain creative adversarial exploration.

5. Formal Methodologies, Metrics, and Evaluation Frameworks

Effective cyber red teaming depends on rigorous methodologies for threat modeling, scenario construction, and outcome evaluation. Key formalizations used in red-team automation and analysis include:

• Multi-phase attack modeling (graphs/trees, e.g. Hierarchical Attack Representation Model (Enoch et al., 2020))
• Security metrics such as Number of Attack Scenarios (NAS), Return on Attack (ROA), Attack Impact (AIM)

  $$\text{ROA} = \sum_{ap \in \mathbb{AP}} \sum_{h \in ap}\frac{r_h}{ac_h}$$

• Reinforcement learning frameworks for simulating adaptive adversaries and defenders, with value and cost functions:

  $$f(x) = \left\{ \begin{array}{ll} 0 & \text{if } x \leq 1175 \ \frac{x - 1175}{4350 - 1175} & \text{if } 1175 < x < 4350 \ 1 & \text{if } x \geq 4350 \end{array} \right.$$

  (for cost in cyber alert queuing (Shah et al., 2018))

• Bidirectional macro-level and micro-level red teaming, connecting system-wide lifecycle risk assessment to model-specific boundary-testing (Majumdar et al., 7 Jul 2025)
• Scaled comparative tool analysis with weighted questionnaires and expert surveys for selection/adoption (Landauer et al., 28 Aug 2024):

  $$s_{\text{tool, role}} = \frac{1}{3 \cdot |Q_{\text{tool}}|} \cdot \sum (\text{score}(q) \cdot \overline{w}_{\text{subcat}(q), \text{role}})$$

These approaches support the repeatable, scalable, and transparent evaluation necessary to institutionalize red team findings and inform both remediation and disclosure.

6. Organizational Structures and Rules of Engagement

Mature cyber red team operations are embedded within repeatable processes featuring formal rules of engagement (RoEs), explicit threat model definition, mutual accountability, and coordinated vulnerability disclosure (Sinha et al., 14 Sep 2025, Majumdar et al., 7 Jul 2025). Table 1 in (Sinha et al., 14 Sep 2025) outlines mutual responsibilities: the host organization sets mission priorities, defines boundaries and desired outcomes, and the red team conforms to these boundaries and reports vulnerabilities as per CVD protocol. This structure is essential for both regulatory alignment and to avoid collateral risk/organizational harm during exercise.

Hybrid red-teaming—integrating AI-specific expertise and classical cyber operations—relies on these mature structures to ensure both attack authenticity (emulating adversaries) and responsible/ethical engagement, especially as systems become more complex and societal impact broadens.

7. Future Challenges and Integration with AI

The convergence of cyber and AI red teaming is forecast to shape the next generation of security assessment. Core challenges include:

• Integrating AI attack surface exploration—covering emergent and persistent (“unpatchable”) vulnerabilities—into classical risk frameworks (Sinha et al., 14 Sep 2025)
• Developing scalable, domain-specific automated tooling that supports both model-centric and system/lifecycle-wide red-teaming at scale (Liu et al., 19 Aug 2025, Majumdar et al., 7 Jul 2025)
• Managing dual-use risks as adversarial automation becomes accessible not only to ethical teams but also malicious actors (Abuadbba et al., 16 Jun 2025)
• Advancing hybrid training frameworks (e.g., Digital Twin + LLM-based education (Barletta et al., 23 Jul 2025)), behavior-driven adversarial simulations, and “red teaming of red teaming” to uncover systemic flaws in current approaches

This evolution requires interdisciplinary, multifunctional teams spanning technical, psychological, ethical, and policy domains, the codification of red teaming into proactive and reactive pillars, and the continual adaptation of methodologies to emerging socio-technical and operational realities.

References Table: Exemplary Red Team Methodologies and Tools

Paper/Framework	Domain/Approach	Notable Contributions
(Shah et al., 2018)	RL/Game Theory for Alert Systems	Adversarial RL, double oracle retrain
(Enoch et al., 2020)	HARMer (Attack Graph Modeling)	Automated planning/evaluation, HARM
(Landauer et al., 28 Aug 2024)	Open-Source Tool Comparison	Technical/user-weighted ranking
(Al-Azzawi et al., 25 Mar 2025)	AI-driven Red Team Taxonomy	RL, GAN, SVM, clustering methods
(Majumdar et al., 7 Jul 2025)	Red Teaming AI Red Teaming	Macro/micro, system-theory approach
(Sinha et al., 14 Sep 2025)	AI as Cyber Red Team Evolution	Unified methodology, RoE emphasis

In summary, cyber red teaming fuses adversary emulation, structured risk evaluation, and adaptive automation under a rigorous, accountable, and collaborative operational regime to advance organizational and societal security postures in the face of increasingly sophisticated, AI-augmented threats.