Part 2 of 4: Tranchulas Threat Intelligence Series
Executive Summary
Series Overview:
- Part 1: Understanding AI-powered threats and real-world attack cases
- Part 2: Red team methodologies for AI threat simulation — You are here
Coming in this series:
- Part 3: Defensive strategies and AI-resilient security architectures
- Part 4: Strategic roadmap and future threat evolution
Introduction: The Evolution of Red Team Methodologies
At Tranchulas, our red team operations have always focused on realistic adversary simulation that tests not just technical controls but organizational resilience under pressure. However, the emergence of AI-powered threats has fundamentally challenged our traditional approaches, forcing us to develop new methodologies that can accurately simulate adversaries who leverage artificial intelligence to enhance their capabilities.
The challenge is not simply adding AI tools to existing red team frameworks—it requires reconceptualizing how attacks are planned, executed, and sustained. AI enables a level of automation, adaptation, and scale that transforms every phase of the attack lifecycle, from initial reconnaissance through final impact and persistence. Traditional red team exercises, designed around human-driven attack patterns, cannot adequately test organizational defenses against threats that can learn, adapt, and evolve faster than human response capabilities.
Our extensive experience conducting red team exercises across enterprise and government environments has revealed that organizations implementing robust defenses against traditional threats often remain vulnerable to AI-enhanced attacks. The adaptive nature of these threats, their ability to operate at unprecedented scale, and their capacity to circumvent detection through continuous evolution require testing methodologies that can simulate these unique characteristics.
This analysis draws from our practical experience developing and implementing AI-enhanced red team methodologies, cutting-edge research from academic institutions and industry organizations, and real-world testing results that demonstrate the effectiveness of various simulation approaches. We examine not just how to conduct AI-enhanced red team exercises, but how to structure ongoing validation programs that can keep pace with rapidly evolving threat landscapes.
The implications extend beyond technical testing to fundamental questions about organizational preparedness and resilience. AI-powered threats can overwhelm traditional incident response capabilities, exploit human psychology in sophisticated ways, and persist in environments despite conventional remediation efforts. Testing organizational resilience against these threats requires methodologies that can simulate not just the technical aspects of AI-powered attacks but their psychological and operational impacts as well.
The Cloud Security Alliance Framework: Twelve Agentic Threat Categories
Understanding Agentic AI Threats
The Cloud Security Alliance’s Red Teaming Testing Guide for Agentic AI Systems represents a significant advancement in structured approaches to AI threat simulation [1]. Unlike traditional generative AI models that simply respond to prompts, agentic AI systems can independently plan, reason, and execute actions in real-world or virtual environments. This autonomy creates entirely new attack surfaces and exploitation techniques that require specialized testing methodologies.
The framework identifies twelve high-risk threat categories that red teams must consider when simulating AI-powered attacks. Each category represents a distinct class of vulnerabilities and attack vectors that emerge from the autonomous nature of AI systems. Understanding these categories is essential for developing comprehensive testing programs that can validate organizational defenses against the full spectrum of AI-powered threats.
The significance of this framework extends beyond simple categorization—it provides a structured approach to threat modeling that accounts for the unique characteristics of AI systems. Traditional threat modeling approaches, based on static system analysis and predetermined attack paths, are insufficient for AI systems that can modify their behavior dynamically and create new attack vectors through autonomous decision-making.
Authorization and Control Hijacking
Authorization and control hijacking represents one of the most critical threat categories, focusing on exploiting gaps between permissioning layers and autonomous agents. Our red team exercises have consistently revealed that organizations implement robust access controls for human users while failing to adequately secure the interfaces and decision-making processes of AI systems.
The technical methodology for testing authorization hijacking involves simulating attacks that manipulate AI agents into performing unauthorized actions or escalating privileges beyond their intended scope. This includes testing scenarios where AI agents are tricked into bypassing access controls, manipulating permission inheritance in multi-agent environments, exploiting trust relationships between AI systems and human users, and leveraging AI decision-making processes to circumvent approval workflows.
Our testing has revealed several common vulnerabilities in this category. Organizations often implement AI systems with excessive privileges, assuming that the AI’s programming will prevent misuse. However, sophisticated attackers can manipulate AI reasoning processes to justify unauthorized actions or exploit edge cases in permission logic that the AI interprets differently than intended.
The red team methodology for testing authorization hijacking includes developing test scenarios that challenge AI permission boundaries, creating adversarial inputs designed to manipulate AI decision-making processes, implementing monitoring systems to track AI privilege usage during testing, and documenting cases where AI systems exceed their intended authorization scope.
Checker-Out-of-the-Loop Attacks
The “checker-out-of-the-loop” threat category addresses scenarios where AI systems bypass safety checkers or human oversight during sensitive actions. This is particularly concerning in environments where AI agents are granted significant autonomy to improve efficiency and responsiveness, but where critical safeguards can be circumvented through sophisticated manipulation.
Our red team testing in this category focuses on identifying scenarios where AI systems can be manipulated to operate without appropriate oversight. This includes testing the effectiveness of safety mechanisms under adversarial conditions, evaluating the robustness of human-in-the-loop controls, assessing the reliability of automated safety checkers, and identifying conditions where AI systems might reasonably bypass safety controls.
The methodology involves creating test scenarios that challenge safety mechanisms without triggering obvious red flags. This requires sophisticated understanding of how AI systems make decisions about when to engage safety controls and how those decisions can be influenced through subtle manipulation of inputs or environmental conditions.
Our testing has revealed that many organizations implement safety controls that work effectively under normal conditions but can be circumvented when AI systems encounter carefully crafted adversarial scenarios. The challenge is that these bypasses often appear reasonable from the AI’s perspective, making them difficult to detect through traditional monitoring approaches.
Goal Manipulation and Adversarial Objectives
Goal manipulation attacks represent a sophisticated form of adversarial input designed to redirect agent behavior toward malicious objectives. These attacks go beyond simple prompt injection, involving complex manipulation of AI reasoning processes to achieve outcomes that appear legitimate while serving adversarial purposes.
Our red team methodology for testing goal manipulation involves developing sophisticated adversarial inputs that can gradually shift AI behavior over time. This includes creating scenarios where AI objectives are subtly modified through environmental manipulation, testing the robustness of AI goal-setting and validation processes, evaluating the effectiveness of AI systems at detecting conflicting or malicious objectives, and assessing the persistence of goal manipulation across AI system restarts or updates.
The technical challenge of goal manipulation testing lies in creating inputs that are sophisticated enough to fool advanced AI systems while remaining realistic enough to represent actual threat scenarios. This requires deep understanding of AI reasoning processes and the ability to craft inputs that exploit specific vulnerabilities in AI decision-making logic.
Our testing has demonstrated that even well-designed AI systems can be susceptible to subtle goal manipulation that gradually shifts their behavior over time. The most effective attacks in this category involve incremental changes that build trust and establish patterns before introducing more significant manipulations.
Knowledge Base Poisoning
Knowledge base poisoning attacks target the long-term memory and shared knowledge spaces that AI systems rely upon for decision-making. This threat category is particularly insidious because it can affect AI behavior over extended periods, creating persistent vulnerabilities that may not be immediately apparent.
The red team methodology for testing knowledge base poisoning involves simulating attacks that corrupt AI training data, reference materials, and knowledge repositories. This includes testing the integrity controls for AI knowledge sources, evaluating the effectiveness of data validation and verification processes, assessing the impact of corrupted knowledge on AI decision-making, and identifying methods for detecting and remediating knowledge base corruption.
Our testing has revealed that organizations often fail to implement adequate integrity controls for AI training data and reference materials. This creates opportunities for attackers to influence AI behavior through strategic data manipulation that may not be detected until the corrupted knowledge affects critical decisions.
The challenge of knowledge base poisoning testing lies in creating realistic corruption scenarios that demonstrate the potential impact without causing actual harm to production systems. This requires careful isolation of test environments and sophisticated simulation of knowledge corruption effects.
Multi-Agent Exploitation Scenarios
The proliferation of AI agents within enterprise environments has created new opportunities for sophisticated multi-agent exploitation scenarios. These attacks involve spoofing, collusion, or orchestration-level attacks that leverage the interconnected nature of AI systems to achieve objectives that would be impossible through single-agent manipulation.
Our red team methodology for testing multi-agent exploitation includes simulating agent spoofing attacks where malicious AI agents impersonate legitimate systems, testing collusion scenarios where multiple compromised agents coordinate their activities, evaluating the security of inter-agent communication protocols, and assessing the effectiveness of agent authentication and verification mechanisms.
The technical complexity of multi-agent testing requires sophisticated simulation environments that can model realistic agent interactions while providing the control necessary for systematic testing. This includes developing test frameworks that can simulate large numbers of AI agents, creating realistic communication patterns between agents, and implementing monitoring systems that can track complex multi-agent attack scenarios.
Our testing has consistently revealed that organizations implement security controls that focus on individual agent behavior while failing to detect coordinated activities across multiple agents. This creates opportunities for attackers to distribute malicious activities across multiple agents, making detection and attribution significantly more challenging.
Enhanced MITRE ATT&CK Framework for AI Threats
Adapting Traditional TTPs for AI Enhancement
The MITRE ATT&CK framework has long served as the foundation for understanding and categorizing adversary tactics, techniques, and procedures. However, the emergence of AI-powered threats requires significant adaptation and expansion of this framework to account for the unique capabilities and attack vectors that artificial intelligence enables.
Our red team operations have identified specific areas where traditional TTPs are being enhanced by AI, as well as entirely new categories of techniques that have no analog in conventional cyber attacks. The integration of AI into the ATT&CK framework is not simply a matter of adding new techniques to existing categories—it requires a fundamental reconceptualization of how attacks are planned, executed, and sustained.
AI enhancement occurs across all fourteen tactics in the MITRE ATT&CK framework, but with varying degrees of impact and sophistication. Some tactics, such as reconnaissance and initial access, are being revolutionized by AI capabilities, while others, such as persistence and defense evasion, are being incrementally enhanced through automation and adaptive techniques.
Initial Access: AI-Powered Entry Points
The initial access tactic has been fundamentally transformed by AI capabilities, with threat actors leveraging machine learning and natural language processing to create highly sophisticated and targeted entry point attacks. Our red team testing in this area focuses on simulating the most advanced AI-powered initial access techniques to validate organizational defenses.
AI-generated spear phishing represents a quantum leap in social engineering sophistication. Our testing methodology involves using large language models to analyze publicly available information about targets and create highly personalized phishing messages that incorporate specific details about the target’s role, responsibilities, and interests. These messages are often indistinguishable from legitimate communications, making them extremely difficult for both human users and automated systems to detect.
The red team approach to testing AI-generated phishing includes developing realistic target profiles based on publicly available information, creating personalized phishing content using AI tools, implementing delivery mechanisms that mimic legitimate communication channels, and measuring the effectiveness of both human and automated detection systems.
Deepfake social engineering attacks represent an entirely new category of initial access technique that requires specialized testing methodologies. Our red team exercises include simulating voice cloning attacks against help desk and support personnel, testing video deepfake attacks in virtual meeting environments, evaluating the effectiveness of multi-modal deepfake attacks that combine audio and video, and assessing organizational procedures for verifying identity in digital communications.
The technical challenge of deepfake testing lies in creating realistic simulations that demonstrate the threat without causing actual harm or confusion. This requires careful coordination with target organizations and sophisticated technical capabilities for generating convincing deepfake content.
Execution: Adaptive and Autonomous Implementation
The execution tactic has been enhanced by AI through the development of adaptive malware and autonomous attack systems that can modify their behavior based on the target environment. Our red team testing in this area focuses on simulating attacks that can adapt to specific system configurations and defensive measures.
Adaptive malware execution testing involves deploying simulated malware that can analyze the target environment and modify its execution strategy accordingly. This includes testing malware that can detect security tools and adjust behavior to avoid detection, evaluating systems that can identify system configurations and optimize exploitation techniques, and assessing the effectiveness of malware that can learn from failed execution attempts.
The methodology for testing adaptive execution includes developing malware simulators that can demonstrate adaptive behavior without causing actual harm, creating test environments that accurately represent production systems, implementing monitoring systems that can track adaptive behavior patterns, and documenting the effectiveness of various adaptive techniques against different defensive configurations.
Autonomous exploitation represents a significant advancement in execution capabilities, with AI systems capable of identifying and exploiting vulnerabilities without human intervention. Our testing methodology includes simulating autonomous vulnerability discovery and exploitation, evaluating the speed and effectiveness of AI-powered exploitation techniques, and assessing the ability of defensive systems to detect and respond to autonomous attacks.
Persistence: AI-Powered Long-Term Access
The persistence tactic has been revolutionized by AI through the development of intelligent persistence mechanisms that can adapt to changing environments and defensive measures. Our red team testing focuses on simulating persistence techniques that leverage AI capabilities to maintain long-term access while evading detection.
AI agent implantation represents a novel form of persistence where malicious AI agents are embedded within legitimate AI systems or processes. Our testing methodology includes simulating the deployment of malicious AI agents within target environments, evaluating the effectiveness of AI agent detection and removal procedures, assessing the ability of malicious agents to maintain persistence across system updates and restarts, and testing the impact of AI agent persistence on system performance and behavior.
The technical challenge of AI agent persistence testing lies in creating realistic simulations that demonstrate the threat without compromising production systems. This requires sophisticated isolation techniques and careful coordination with target organizations to ensure that testing does not interfere with legitimate operations.
Knowledge base poisoning represents another critical persistence technique that our red team exercises regularly test. This includes simulating attacks that corrupt AI training data and reference materials, evaluating the persistence of knowledge corruption across AI system updates, assessing the effectiveness of data integrity controls and validation procedures, and testing the ability of organizations to detect and remediate knowledge base corruption.
Defense Evasion: The AI Arms Race
Defense evasion has been fundamentally transformed by AI capabilities that enable real-time adaptation to defensive measures. Our red team testing in this area focuses on simulating the most advanced AI-powered evasion techniques to validate the effectiveness of defensive systems.
Polymorphic code generation represents a significant advancement in evasion capabilities, with AI systems capable of continuously rewriting malware code to avoid signature-based detection. Our testing methodology includes simulating AI-powered code generation that can create functionally equivalent code with different signatures, evaluating the effectiveness of signature-based detection against polymorphic threats, and assessing the ability of behavioral analysis systems to detect polymorphic malware.
Behavioral mimicry involves AI systems that can analyze normal system and user behavior patterns and modify their activities to blend in with legitimate operations. Our red team testing includes simulating AI systems that can learn from observed behavior patterns, evaluating the effectiveness of behavioral analysis systems against mimicry attacks, and assessing the ability of AI-powered evasion to defeat advanced detection systems.
The methodology for testing behavioral mimicry includes developing AI systems that can analyze and replicate normal behavior patterns, creating test scenarios that challenge behavioral detection systems, implementing monitoring systems that can track mimicry effectiveness, and documenting the limitations of various behavioral analysis approaches.
Continuous Validation and Adaptive Testing
The Need for Continuous Assessment
The dynamic nature of AI-powered threats requires a fundamental shift from periodic security assessments to continuous validation and adaptive testing methodologies. Traditional red team exercises, conducted annually or quarterly, are insufficient for environments where threats can evolve and adapt in real-time.
Our approach to continuous validation involves implementing ongoing assessment programs that provide real-time feedback on organizational resilience against evolving AI threats. This includes developing automated testing frameworks that can continuously probe defensive systems, implementing adaptive testing methodologies that evolve based on observed defensive responses, creating feedback loops that ensure testing results inform defensive improvements, and establishing metrics that provide ongoing visibility into defensive effectiveness.
The technical challenge of continuous validation lies in developing testing frameworks that can operate safely in production environments while providing meaningful assessment of defensive capabilities. This requires sophisticated automation, careful risk management, and close coordination with defensive teams to ensure that testing enhances rather than compromises security.
Simulation-Based Testing Approaches
Simulation-based testing represents a critical component of continuous validation, enabling organizations to test their defenses against a wide range of AI-powered attack scenarios without the risks associated with live testing. Our simulation frameworks incorporate machine learning algorithms that can generate novel attack variants, ensuring that testing remains relevant as threat landscapes evolve.
The methodology for simulation-based testing includes developing realistic simulation environments that accurately represent production systems, creating AI-powered attack simulators that can demonstrate various threat scenarios, implementing monitoring and measurement systems that can assess defensive effectiveness, and establishing feedback mechanisms that ensure simulation results inform defensive improvements.
Our simulation frameworks are designed to be adaptive, learning from defensive responses and evolving their attack techniques accordingly. This creates a dynamic testing environment that can keep pace with evolving threats while providing ongoing validation of defensive capabilities.
Portfolio-Wide Assessment Strategies
Portfolio-wide assessments are essential for understanding the cumulative risk posed by AI systems across an organization. Individual AI systems may appear secure when assessed in isolation, but their interactions and dependencies can create vulnerabilities that only become apparent through comprehensive analysis.
Our methodology for portfolio-wide assessment includes systematic evaluation of AI system interactions and dependencies, assessment of cumulative risk across multiple AI systems, identification of potential cascade effects and systemic vulnerabilities, and evaluation of organizational capabilities for managing AI-related risks at scale.
The challenge of portfolio-wide assessment lies in developing methodologies that can handle the complexity of modern AI deployments while providing actionable insights for risk management. This requires sophisticated analysis capabilities and deep understanding of AI system architectures and interactions.
Implementation Guidance for Red Team Programs
Building AI-Enhanced Red Team Capabilities
Organizations seeking to implement AI-enhanced red team capabilities must invest in both technical tools and human expertise. The complexity of AI-powered threats requires red team professionals who understand both traditional attack techniques and the unique capabilities that AI enables.
Our recommendations for building AI-enhanced red team capabilities include recruiting red team professionals with AI and machine learning expertise, providing comprehensive training on AI attack techniques and simulation methodologies, investing in AI-powered testing tools and simulation frameworks, and establishing partnerships with academic institutions and research organizations to stay current with emerging threats.
The development of internal AI red team capabilities requires significant investment in both technology and training. Organizations must be prepared to make long-term commitments to building and maintaining these capabilities as the threat landscape continues to evolve.
Metrics and Measurement Frameworks
Effective red team programs require robust metrics and measurement frameworks that can assess the effectiveness of both testing methodologies and defensive capabilities. Traditional red team metrics, focused on simple success/failure outcomes, are insufficient for evaluating organizational resilience against adaptive AI threats.
Our recommended metrics framework includes technical measures such as detection rates for AI-powered attacks, response times for adaptive threats, and effectiveness of AI-powered defensive systems. Operational measures should include assessment of human performance under AI-enhanced attack scenarios, evaluation of organizational decision-making under pressure, and measurement of recovery capabilities following AI-powered incidents.
The challenge of measuring AI red team effectiveness lies in developing metrics that can account for the adaptive and evolving nature of AI threats while providing actionable insights for improvement. This requires sophisticated measurement frameworks and ongoing refinement based on experience and evolving threat landscapes.
Preparing for Part 3: Defensive Strategies
The red team perspective provides critical insights into the nature of AI-powered threats and the effectiveness of various defensive approaches. Our testing has consistently demonstrated that traditional security controls, while still important, are insufficient for addressing the unique challenges posed by AI-enhanced attacks.
The adaptive nature of AI threats, their ability to operate at unprecedented scale and speed, and their capacity to exploit human psychology in sophisticated ways require defensive strategies that can match these capabilities. Organizations must move beyond reactive detection and response to embrace proactive, adaptive defense strategies that can anticipate and counter AI-powered attacks.
In Part 3 of this series, we will examine how defensive strategies must evolve to address AI-powered threats. We’ll explore the adaptation of zero trust principles for AI-enabled environments, analyze the role of AI-powered defensive systems in creating effective security architectures, and provide practical guidance for implementing continuous monitoring and adaptive response capabilities.
The insights gained from red team testing provide the foundation for understanding what defensive strategies will be most effective against AI-powered threats. The organizations that successfully implement these defensive strategies will be those that understand the threat landscape through the lens of realistic adversary simulation and continuous validation.
Part 4 will present a comprehensive strategic roadmap for organizations seeking to build long-term resilience against evolving AI threats. We’ll examine future threat evolution, analyze investment priorities and resource allocation strategies, and provide detailed guidance for organizational transformation in the AI era.
The red team perspective is essential for understanding not just what AI-powered threats look like today, but how they will evolve and what capabilities organizations will need to defend against them effectively. The time for preparation is now, and the organizations that act decisively will be best positioned to thrive in an increasingly AI-powered threat environment.
References
[1] Cloud Security Alliance. (2025, June 13). Red Teaming Testing Guide for Agentic AI Systems. Campus Technology. Retrieved from https://campustechnology.com/articles/2025/06/13/cloud-security-alliance-offers-playbook-for-red-teaming-agentic-ai-systems.aspx
About Tranchulas: We are a global cybersecurity leader delivering advanced offensive and defensive solutions, compliance expertise, and managed security services. With specialized capabilities addressing ransomware, AI-driven threats, and shifting compliance demands, we empower enterprises and governments worldwide to secure operations, foster innovation, and thrive in today’s digital-first economy. Learn more at tranchulas.com.