Automated Red Teaming: Capabilities, Pros/Cons, and Latest Trends

What is automated red teaming?

Automated red teaming uses software to simulate cyberattacks and test security defenses, helping organizations find and fix vulnerabilities more efficiently. It automates tasks like credential harvesting, system enumeration, and privilege escalation to test security posture in a continuous, scalable manner. Beyond traditional systems, automated red teaming can also be used for AI systems, where it tests for risks like data poisoning or prompt injection in generative models.

Automated red teaming is not intended to replace human red teams. Instead, it enhances security validation by allowing frequent, consistent, and scalable attack simulations. Automated platforms can replicate entire attack chains, integrate with security telemetry, and generate actionable reports more quickly than manual efforts alone. This enables organizations to test their security posture proactively and respond faster to new vulnerabilities and attack techniques.

This is part of a series of articles about AI red teaming

How automated red teaming differs from traditional red teaming

Traditional red teaming typically involves skilled security professionals emulating sophisticated threat actors over defined timeframes. The process is often manual, with practitioners tailoring scenarios dynamically, leveraging creativity, and improvising in response to unique IT environments. Human-led exercises prioritize subtlety and adaptability, often staying undetected for weeks. While this approach provides deep contextual insights, it is resource-intensive, infrequent, and may lack standardization across engagements.

Automated red teaming delivers broad and repeatable attack simulations using pre-programmed playbooks and frameworks. Automation emphasizes repeatability and speed, enabling tests to run on demand, sometimes continuously. These tools scale quickly across large environments and can assess vast attack surfaces within hours. While automated attacks may lack the creativity of human operators, they ensure that security teams can consistently validate controls against hundreds of real-world attack techniques with limited manual overhead.

Human red teams increasingly integrate automated red teaming tools into their operations to enhance efficiency and coverage. Automation handles repetitive tasks, such as scanning for exposed services, attempting known exploits, or harvesting credentials, freeing up human operators to focus on complex, adaptive strategies. During engagements, red teams may use automation to pre-stage access or simulate specific phases of an attack chain, allowing them to validate assumptions or prepare environments for deeper manual exploitation.

What can be automated in red teaming 

1. Credential harvesting and lateral movement

Credential harvesting, the process of collecting usernames, passwords, and authentication tokens, is commonly automated using specialized tools. Automated systems launch phishing campaigns, simulate password spraying, or deploy credential theft malware to imitate real-world tactics. Once credentials are gathered, scripts and platforms simulate lateral movement through the environment, mimicking how attackers expand access by pivoting between systems and escalating privileges across domains.

Automation allows the red team to run multiple credential harvesting and lateral movement scenarios swiftly and at scale. With integrations into directory services and endpoint management systems, automated simulations test a variety of authentication vectors. This exposes gaps in user awareness, detection coverage, and network segmentation policies.

2. Full kill-chain simulations

Automated red teaming platforms can orchestrate full kill-chain simulations, replaying end-to-end attack scenarios from initial access to impact. These simulations incorporate techniques such as phishing, exploitation, privilege escalation, lateral movement, command and control, and data exfiltration in coordination. By automating all attack phases, defenders gain holistic visibility into where preventative and detective controls fail or succeed in stopping adversary progress.

Running full automated kill-chain simulations increases testing frequency and consistency. Security teams receive actionable feedback on how well their controls, detection logic, and incident response plans perform against sophisticated, multi-stage threats. This approach reveals breakdowns that piecemeal testing would miss.

3. Custom attack chains and threat-adapted scenarios

Automated red teaming solutions enable the creation of custom attack chains tailored to specific organizations, threat models, or current intelligence. Security teams can design workflows that replicate the tactics, techniques, and procedures (TTPs) of particular adversaries, such as ransomware groups or nation-state actors. These tailored scenarios provide more relevant insights into the effectiveness of security controls against threats most likely to target the organization.

By updating and composing attack modules, red teams generate threat-adapted scenarios that address emerging risks or newly discovered attack methods. Automation allows security leaders to validate controls against evolving threats without waiting for the next manual engagement.

4. Continuous exposure validation

Continuous exposure validation refers to the ongoing, automated simulation of attacks to identify exposures and validate the security posture in real time. Automated red teaming platforms can be scheduled to run scenarios on a daily, weekly, or ad-hoc basis. This ensures that new assets, users, or configurations are instantly tested for vulnerabilities and that defenses remain effective despite constant change in the environment.

Continuous validation helps organizations move from periodic compliance-based testing to dynamic risk management. Automated feedback loops allow security teams to address weaknesses as they arise, maintaining a constant state of readiness.

Pros and cons of automated red teaming 

Automated red teaming offers unique advantages in scalability and efficiency but also comes with notable limitations. Understanding both sides helps organizations make informed decisions about integrating automation into their security validation processes.

Pros:

• Scalability: Automated platforms can simulate attacks across large environments without requiring proportional increases in personnel.
• Repeatability: Tests can be run frequently with consistent results, making it easier to track improvements over time.
• Speed: Attack simulations can execute within minutes or hours, allowing for faster feedback and remediation.
• Coverage: Automation enables testing across a wide range of attack techniques, often covering more ground than manual teams can within a limited timeframe.
• Integration: Many platforms integrate with existing SIEM, SOAR, and EDR tools to provide real-time detection and response validation.
• Cost efficiency: By reducing the reliance on human experts for every test, organizations can lower the cost per engagement.

Cons:

• Limited creativity: Automated tools follow predefined playbooks and may miss novel attack paths or behaviors a human operator could uncover.
• Context blindness: Automation may not fully understand the business impact or context of targets, leading to less meaningful results.
• Tool dependence: Effectiveness is tied to the quality and currency of the platform’s TTPs and threat intelligence feeds.
• False confidence: Relying solely on automation can give a false sense of security, especially if the simulations are not kept up to date with emerging threats.
• Lack of persistence: Automated attacks are usually short-lived, missing insights that come from prolonged, stealthy engagements common in real-world intrusions.

Related content: Read our guide to AI red teaming tools

Emerging trends in automated red teaming 

Here are some of the latest trends driving the evolution of automated red teaming.

Continuous automated red teaming (CART)

Continuous automated red teaming (CART) takes automation further by enabling persistent, always-on attack simulations across infrastructure and applications. Unlike periodic exercises, CART platforms operate autonomously, emulating adversarial behavior in real time without human intervention. This approach integrates attack simulation with ongoing monitoring, providing timely assessments of how new assets, updates, or configuration changes impact security posture.

CART allows organizations to identify and remediate exposures as they arise, rather than retroactively responding to red team findings after an annual or quarterly exercise. By embedding automation within development and production workflows, security teams can keep pace with rapid digital transformation.

Automated red teaming for AI and ML systems

AI and ML systems present unique and often poorly understood attack surfaces, requiring specialized red teaming approaches. Automated tools can replicate threat scenarios specific to these environments, such as adversarial input attacks, model inversion, data poisoning, and exploitation of AI supply chains. Automation enables frequent, systematic testing that accounts for the rapid evolution and complexity of ML pipelines, data flows, and deployment environments.

Automated red teaming for AI/ML allows organizations to simulate sophisticated attacks, validate defenses, and identify resilience gaps throughout model development and deployment. These automated simulations are essential in sectors like finance or healthcare, where AI integrity, fairness, and reliability are mission-critical.

Automated progressive red teaming

Automated progressive red teaming (APRT) introduces a structured and learnable approach to red teaming for large language models (LLMs). Unlike earlier automated efforts that lacked a systematic framework, APRT operates through a multi-step process using three specialized modules: an intention-expanding model to generate varied attack samples, an intention-hiding model to create deceptive prompts, and a filtering module that manages prompt diversity and removes ineffective samples.

These components work together in repeated cycles to explore and exploit vulnerabilities in LLMs more thoroughly than one-off attacks. The progressive nature of APRT allows it to refine attacks over multiple rounds, making it more effective at revealing unsafe behaviors that simpler red teaming methods may miss.

To evaluate effectiveness, APRT introduces a new metric called Attack Effectiveness Rate (AER), which focuses on how often an LLM produces unsafe but seemingly helpful responses. This metric correlates well with human assessments, offering a more accurate picture of real-world risk. Experiments show APRT can consistently provoke unsafe outputs across both open- and closed-source models, demonstrating its ability to transfer attack strategies across different systems.

Integration with continuous security posture management

Automated red teaming is increasingly integrated with broader continuous security posture management platforms, such as continuous threat exposure management (CTEM) and breach and attack simulation (BAS) solutions. These integrations enable organizations to correlate red team findings with vulnerability management, configuration compliance, and risk assessments. By combining automated attack simulations with posture visibility, organizations gain a comprehensive, prioritized view of their security gaps.

For example, when automated red teaming reveals an exploitable pathway, CTEM platforms can immediately trigger workflow-driven remediation and reassess exposures. Similarly, integration with BAS solutions ensures that attack simulations align with real-world threat intelligence and evolving adversary TTPs. The result is a unified, proactive approach to risk reduction, breaking down silos between security teams and improving overall resilience.

Best practices for automated red teaming programs 

Clearly define and measure KPIs

Establishing clear key performance indicators (KPIs) is essential to evaluate the impact and maturity of automated red teaming programs. Common KPIs include time-to-detect, time-to-remediate, number of successful attack paths identified, and percentage of critical findings resolved within a defined SLA. These metrics help security teams assess both the efficacy of simulations and the responsiveness of their remediation processes. 

Effective KPI frameworks also align red teaming efforts with organizational goals. For instance, in highly regulated environments, red team KPIs might focus on compliance-related exposures or data exfiltration scenarios. In contrast, a SaaS provider may prioritize customer-facing system resilience and lateral movement detection.

Choose realistic scenarios to simulate

Automated red teaming is most effective when it mirrors realistic adversary behaviors tailored to the organization’s threat landscape. Scenarios should be based on up-to-date threat intelligence, including tactics, techniques, and procedures (TTPs) used by known threat actors. Simulations targeting exposed APIs, cloud misconfigurations, lateral movement across identity systems, and phishing-based initial access are all relevant to modern enterprise environments. 

Avoiding abstract or overly generic attacks ensures findings are meaningful and tied to actual risk. Scenario realism also depends on the context of the environment being tested. Red team simulations for cloud-native infrastructures should reflect service-specific attack chains, such as abusing IAM roles in AWS or misconfiguring workload identities in Kubernetes.

Enforce strict guardrails and safe-execution policies

Automated red teaming operations must be subject to strict guardrails to ensure attack simulations do not produce unintended consequences. Safe-execution policies should define what is in-scope, when and where attacks occur, and which accounts or systems are off-limits. Controls such as rate limiting, automatic deconfliction, and approval workflows minimize the risks of disrupting production services or impacting sensitive data.

Establishing and enforcing these guardrails is essential for maintaining trust with stakeholders and avoiding collateral damage. Detailed documentation, training, and automated controls reduce the likelihood of errors or unapproved actions.

Integrate automated findings with human-led red team reviews

Automated red teaming should supplement, not replace, human expertise. Integrating automated findings with human-led reviews ensures that complex, context-dependent issues receive adequate attention. Analysts can validate, investigate, and escalate automated results, as well as identify attack paths or vulnerabilities that automation might miss. This hybrid approach delivers a more complete assessment of security posture.

Collaboration between automated and manual teams also accelerates remediation, allows for more nuanced reporting, and supports knowledge transfer. Periodic human review of automated scenarios ensures that tools remain aligned with organizational risk tolerance and threat models.

Maintain comprehensive logging, replayability, and auditability

Comprehensive logging enables organizations to record every action, event, and outcome during automated red teaming exercises. Detailed logs support forensic analysis, compliance requirements, and troubleshooting when issues arise. Replayability is essential for reproducing scenarios, testing remediations, and verifying fixes without variability in execution.

Auditability ensures all stakeholders (security, legal, and executive teams) can track what was tested, when, and why. Platforms should provide clear reporting, versioning of scenarios, and secure archival of logs for later analysis. These practices not only improve accountability but also allow organizations to learn from past simulations and continuously refine their approach to automated red teaming.

Automated red teaming with Mend.io

Manual red teaming offers valuable insights into model vulnerabilities, but struggles to keep up with rapid AI development. Mend.io’s red teaming solution allows organizations to conduct thousands of automated adversarial attacks, including prompt injection, jailbreaking, and data exfiltration, in just minutes.

Mend.io not only identifies vulnerabilities but also automates the remediation process by detecting risky patterns in AI-generated code and flagging vulnerable open-source dependencies. With in-workflow guidance for developers, Mend.io integrates automated red teaming into the software development life cycle (SDLC), ensuring continuous governance and compliance without sacrificing development speed.