# Yellow Teams: How Companies Are Building AI Offense and Defense to Secure the Future


As artificial intelligence rapidly proliferates across enterprise environments, a growing number of organizations are taking an unconventional approach to security: hiring "yellow teams"—cross-functional groups that simultaneously build both attack and defense tools to probe AI systems for vulnerabilities before adversaries do.


This strategy represents a fundamental shift in how companies think about AI security. Rather than waiting for threats to emerge in the wild, yellow teams are proactively stress-testing AI models and deployment pipelines using red-team attack techniques paired with defensive hardening. The result is a more resilient, battle-tested AI infrastructure—but it also raises questions about containment, knowledge sharing, and whether understanding AI threats has outpaced the security community's ability to defend against them.


## What Are Yellow Teams?


Yellow teams occupy a unique position in the cybersecurity landscape. Unlike traditional red teams (which focus solely on attacks) or blue teams (which focus solely on defense), yellow teams operate across both domains simultaneously within a single organizational unit.


The core model:

  • Half the team designs AI attack tools, jailbreaks, and exploit chains
  • The other half implements detection, mitigation, and hardening measures
  • Both halves collaborate in real-time to close gaps and validate defenses
  • Knowledge flows both directions—offensive insights inform defensive strategy, and defensive constraints guide attack research

    • This approach differs dramatically from bug bounty programs or isolated penetration tests. Yellow teams are embedded in product development, with access to model architectures, training data pipelines, inference infrastructure, and deployment mechanisms. They operate with the assumption that understanding offense is essential to building credible defense.


    ## Background and Context


    The rise of yellow teams reflects mounting anxiety about AI-specific security threats that traditional cybersecurity frameworks don't address. As large language models (LLMs) and other AI systems enter critical workflows—from customer support chatbots to code generation tools to data analysis—the surface area for exploitation has exploded.


    Key drivers:


  • Model extraction attacks: Adversaries can steal proprietary models through prompt engineering and output analysis
  • Prompt injection: Malicious inputs can override model instructions and bypass safety guidelines
  • Data poisoning: Training data can be manipulated to introduce vulnerabilities or biases
  • Adversarial inputs: Small perturbations to input data can cause catastrophic model failures
  • Inference-time exploits: Attackers can manipulate APIs, cache systems, and token limits to trigger unintended behavior

    • Traditional red teams and penetration testers are often insufficient because they lack deep knowledge of how LLMs and deep learning systems work. Yellow teams solve this by embedding machine learning engineers and security researchers in the same unit, allowing each discipline to inform the other.


    ## How Yellow Teams Operate


    A typical yellow team structure includes:


    | Role | Responsibility |

    |------|---|

    | ML Security Researchers | Design novel attack vectors; test model robustness; probe training pipelines |

    | Offensive Engineers | Build automated exploit tools; scale attacks; test attack reliability |

    | Defensive Engineers | Implement mitigations; harden APIs; add monitoring and detection logic |

    | Security Architects | Define threat models; prioritize risks; align findings with business risk |

    | Data Scientists | Analyze model behavior; quantify vulnerability impact; validate fixes |


    The workflow typically follows this pattern:


    1. Threat modeling: Team identifies potential attack vectors (e.g., prompt injection via user input)

    2. Offensive phase: Attackers design and build exploits targeting those vectors

    3. Defensive phase: Defenders implement guardrails, input filters, and detection rules

    4. Testing cycle: Attackers attempt to bypass the new defenses

    5. Iteration: Process repeats until diminishing returns are reached


    ## Technical Attack Surfaces Being Tested


    Yellow teams are systematically probing several high-risk areas:


    Prompt Injection and Jailbreaks

  • Testing whether malicious user input can override system instructions
  • Exploring multi-turn exploitation where harmless initial prompts set up for a malicious follow-up
  • Evaluating defense mechanisms like input sanitization and instruction hierarchy

    • Model Extraction

  • Attempting to steal model weights through inference-time queries
  • Testing whether query patterns can reveal proprietary training techniques
  • Probing API rate limits and logging to identify information leakage

    • Supply Chain Attacks

  • Testing vulnerability in dependencies and third-party integrations
  • Exploring whether malicious fine-tuning data can introduce backdoors
  • Assessing risks in model distillation and transfer learning pipelines

    • Adversarial Robustness

  • Generating adversarial examples that fool vision or NLP models
  • Testing whether slight input modifications trigger unexpected behavior
  • Evaluating model consistency and calibration under edge cases

    • ## Implications for Enterprise Security


    For organizations deploying AI systems at scale, yellow teams have profound implications:


    Risk reduction: Proactive discovery of vulnerabilities before production deployment significantly reduces breach surface area and incident response costs.


    Regulatory confidence: Demonstrating a structured AI security program with red-team validation increasingly matters for compliance audits, especially as governments propose AI governance frameworks.


    Supply chain accountability: Yellow teams can validate third-party AI tools and models before integration, reducing dependency risk.


    Insider threat mitigation: By understanding how to manipulate AI systems, organizations can better defend against malicious employees or contractors.


    However, this creates new risks:

  • Knowledge concentration: Embedding offensive knowledge in a single team creates high-risk, hard-to-replace personnel
  • Escape risk: Documented exploits and tools could be leaked or stolen
  • False confidence: Passing internal tests doesn't guarantee production safety—novel attacks will always emerge
  • Skill gaps: The specialized expertise required is scarce, making it expensive and difficult to scale

    • ## Industry Adoption and Examples


    Large technology companies and well-funded AI labs are leading adoption. Companies like OpenAI, Anthropic, Google DeepMind, and Microsoft have established yellow team or red-team equivalents. Financial institutions, healthcare providers, and defense contractors are beginning to follow.


    Adoption barriers for smaller organizations:

  • Hiring expertise is difficult (few professionals have both deep ML knowledge and security specialization)
  • Building tools and infrastructure requires engineering bandwidth
  • Smaller attack surface may not justify the cost
  • Regulatory pressure is still emerging (so ROI is unclear)

    • ## Recommendations for Defenders


    Organizations deploying AI systems should consider these steps:


  • Start with threat modeling: Before building attack tools, map your specific AI risks based on use case, data sensitivity, and threat actors
  • Invest in observability: Implement comprehensive logging and monitoring of model inputs, outputs, and performance metrics
  • Adopt input validation: Sanitize and validate all user input, especially in systems accepting free-form text
  • Test robustness: Use existing adversarial testing frameworks (e.g., TextAttack, Foolbox) before committing to custom yellow teams
  • Build incrementally: Start with red team assessments before investing in full dual-team infrastructure
  • Share findings responsibly: Participate in industry groups working on AI security standards and share anonymized findings with peers
  • Plan for iteration: AI security is immature—assume your current defenses will be bypassed and plan accordingly

    • ---


    ## HackWire Analysis


    The rise of yellow teams reveals a hard truth: the cybersecurity industry is behind the curve on AI. For decades, we've built security programs around *known* threat models—network intrusion, privilege escalation, data exfiltration. We have playbooks for those.


    But AI threat models are fundamentally different. A model trained on biased data isn't a "vulnerability" in the traditional sense, yet it can cause massive harm. A prompt injection attack doesn't look like SQL injection. Model extraction doesn't follow the kill chain we learned in NIST frameworks.


    Yellow teams are a pragmatic acknowledgment that understanding offense is a prerequisite for defense—and that the gap between the two is wider in AI than anywhere else in cybersecurity.


    The concerning part: most organizations won't have yellow teams. They lack the expertise, budget, and urgency to justify the investment. This creates a bifurcated security landscape where well-funded tech companies and defense contractors gain deep insight into AI threats, while enterprises and startups proceed mostly blind.


    If you're not at a company building AI systems and you're not sure whether your organization has a structured AI security program, now is the time to ask. The difference between a company that proactively tested its LLM for jailbreaks and one that discovers its vulnerability in a PR crisis is enormous.


    Additionally, as AI-powered cyberattacks themselves accelerate—think autonomously-generated phishing campaigns, AI-assisted vulnerability discovery, LLM-generated malware—the defenders who understand *how* AI breaks are the ones who'll still be standing.


    Yellow teams are not a complete solution. They're a beginning. But they represent the serious organizations taking AI security as a first-class problem rather than an afterthought.


    — *HackWire Editorial*


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