Fast Facts on Red Teaming

• AI red teaming is a proactive cybersecurity practice that simulates attacks to detect how large language models might leak or reveal sensitive data through language-based interactions.

• Unlike traditional pen testing, which targets static systems, LLM red teaming navigates the probabilistic and semantic nature of AI, uncovering issues like prompt injection, vector-store poisoning, and function misuse.

• It supports enterprise goals by identifying inference-based risks, ensuring AI regulatory compliance, and building executive confidence for the broader adoption of GenAI.

• A structured workflow, comprising scoping, reconnaissance, exploitation, documentation, remediation, and retesting, ensures that vulnerabilities are systematically addressed and fixed.

• Platforms like Knostic turn red-team insights into proactive governance through continuous simulation and monitoring. They highlight the risks of oversharing in tools like Copilot and Gemini and translate their findings into context-aware policy improvements.

What Is AI Red Teaming?

AI red teaming represents a proactive cybersecurity method that simulates adversarial attacks on AIs to identify and mitigate vulnerabilities before real-world threats exploit them. This is explained in a 2025 Cornell University paper, Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs) that discusses how to create secure and resilient applications with AIs. 

AI penetration testing approaches are built on classic methodologies, integrating frameworks like MITRE ATLAS, which formally maps attack tactics against machine learning systems. However, unlike traditional penetration testing, which targets deterministic systems like APIs or firewalls, another 2025 Cornell University study shows that red teaming operates within a probabilistic and semantic environment. From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs shows that the risk is not a code flaw but what a model might reveal, infer, or connect from seemingly safe content.

One of the main distinctions between AI red teaming and traditional pen testing lies in the systems being tested. Traditional software responds deterministically; the same input always yields the same output. In contrast, LLMs can vary their answers even with identical prompts, introducing unpredictability. Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection shows that this unpredictability poses a unique challenge. Red teamers must not only design malicious prompts but also repeat them, mutate them, and test them across different contexts to uncover leakage or failure. 

Another difference is that traditional pen testing focuses on observable data leakage, like access to a protected file or an administrative panel. In contrast, AI red teaming focuses on inference-based leakage as illustrated in a third Cornell University study paper, Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks. Language models can synthesize sensitive information that is not explicitly stored or accessed, but instead deduced from their training or augmented retrieval systems. Here, even modest prompt manipulations can successfully extract personal data in real-world use cases.

AI red teaming is not a one-time effort. Unlike traditional applications that remain relatively static between releases, LLMs are continuously updated, retrained, and integrated into new workflows. This makes the red teaming process dynamic and ongoing. 

Why Enterprises Need AI Red Teams

Prompt injection testing and data leakage prevention are crucial for protecting both privacy and brand integrity with enterprise AI. Structured red teaming simulates adversarial prompts to reveal how sensitive information might be exposed, demonstrating proactive defense and compliance readiness before attackers strike.

Prevent Prompt Injection, Data Leakage, and Brand Damage.

Prompt injection poses a technical risk to enterprise AI by enabling unauthorized disclosure of sensitive outputs. These exposures can lead not only to privacy violations but also to reputational damage, particularly when leaked responses are shared in customer-facing tools or public channels. 

Red teaming helps detect these failures early. By simulating harmful prompts, GenAI security teams can discover how sensitive data could be revealed. This 2025 study document — AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents — shows how adversarial, language-based attacks can compromise AI assistants. Researchers evaluated their fuzzy prompt framework against two benchmarks, AgentDojo and VWA-adv, achieving attack success rates of 71% and 70%, respectively. These high success rates confirm that LLM agents can be misled into outputting confidential details, even when no explicit API vulnerabilities exist. 

Enterprises must treat prompt injection and inference-based leakage as core threats. Red teaming provides structured, repeatable testing to identify and fix these weaknesses before real attacks occur.

Meet Regulatory Expectations for AI Assurance 

Regulatory frameworks are tightening. The EU AI Act, effective from August 2024, requires mandatory risk management, human oversight, and transparency for high-risk AI systems, including many enterprise LLMs. Companies must show that LLMs behave as expected and don’t expose sensitive data or introduce bias. In the U.S., the NIST AI Risk Management Framework (AI RMF 1.0), launched in January 2023 and subsequently extended with a generative AI profile in July 2024, provides voluntary yet widely adopted guidelines for managing AI risk. It encourages testing for explainability and resilience to adversarial inputs.

Red teaming supports compliance with both. It produces audit-ready documentation showing known risks, attempted attack paths, remediation steps, and validation outcomes. Regulators increasingly look for these logs when evaluating AI deployments. Without red teaming, enterprises lack proof that their LLMs have been tested under realistic threat conditions. That leaves them exposed to fines, reputational damage, and certification delays.

Build Executive Confidence Before Full GenAI Rollout

Executives are eager to adopt generative AI, but they need assurance that deployments won't introduce legal, security, or reputational risks. The 2024 PwC Global CEO survey, Thriving in an age of continuous reinvention, found that 84% of leaders saw efficiency gains from GenAI. However, only about 33% felt confident embedding AI into their core operations, revealing a significant governance gap that must be addressed before a full GenAI rollout.

Red teaming builds that confidence. It enables leadership to identify areas where LLMs are vulnerable, what has been addressed, and how the security posture is improving over time. These results can be presented in metrics like attack success rates, false-positive rates, and drift detection summaries.

Top Attack Vectors to Simulate

LLMs face multiple exploitation risks that extend beyond basic prompt failures. From injection and jailbreak chaining to vector poisoning and function misuse, attackers can manipulate language-based models in subtle yet powerful ways, making red teaming essential for safeguarding enterprise AI deployments.

Prompt Injection

Prompt injection uses crafted text to manipulate an LLM’s behavior. A 2024 University of Illinois paper, introduced a benchmark, IɴᴊᴇᴄAɢᴇɴᴛ to assess the vulnerability of tool-integrated LLM agents to indirect prompt injection (IPI) attacks. It was used to test GPT-4 agents and found a 24% success rate with basic prompt injections. When supplementing with “hacking prompts,” the success rate jumped to nearly 47%, showing that attackers can commonly bypass prompt-based defenses. In Where You Inject Matters, a 2025 report by Helia Estévez, indirect prompt injection attacks bypassed safety filters with a 92% success rate in assistant role contexts and 86% in system role contexts, compared to 52% in standard user-role prompts. These figures indicate how prompt location and context shape vulnerability.

Jailbreak Chaining

Jailbreak chaining aims to bypass built-in system constraints. Attackers create multi-step chains that override safety layers. A 2023 research evaluation, Persistent Pre-training Poisoning of LLMs, showed that persistent backdoor poisoning, which affected only 0.1% of the pre-training data, enabled harmful commands to persist through instruction tuning. Other research discussed in Instructions as Backdoors, used linked instruction poisoning to hijack the assistant’s alignment, achieving over 90% backdoor activation rates in instruction-tuned LLMs. These results confirm the effectiveness of chained instructions in bypassing protections.

Vector-Store Poisoning

Vector-store poisoning works the same way as planting fake documents into a library that your AI assistant uses to answer questions. If even a few of those documents are crafted in a certain way, they can trick the assistant into repeating false or risky information. A 2024 Cornell University study report, PoisonedRAG, found that just five such documents could steer the model to give attacker-chosen responses 90% of the time. That’s why even small amounts of tampering can create significant problems in systems that rely on retrieval. And even small-scale poisoning can significantly distort model outputs, posing substantial risks in retrieval-augmented systems.

Function Misuse

Function misuse occurs when an LLM inadvertently invokes sensitive APIs or internal functions. A 2025 survey analyzed 3,892 method calls and found that general LLMs misused APIs, including inappropriate permission requests or dangerous endpoint calls, in roughly 35% of cases, differ from typical human coding errors. Identifying and Mitigation API Misuse in Large Language Models, proves how language-driven systems can be tricked into misusing privileges. Red-teaming must simulate these scenarios to preempt escalation risks. 

The AI Red Teaming Workflow

Red teaming AIs follows a structured and repeatable lifecycle, from scoping assets and simulating attacks to documenting failures and enforcing fixes. This approach ensures that AI systems are continuously hardened against real-world threats through methodical testing and validation.

1. Scope Assets, Data Domains, and User Roles

The process begins with scoping. You must identify which AI models and platforms are in use, define data domains, including customer records, internal documents, and code repositories, and map user roles: regular users, admins, and finance teams. Scoping ensures that testing covers the most sensitive and business-critical assets. Microsoft’s LLM red-teaming guidance, Planning red teaming for large language models (LLMs) and their applications, emphasizes the importance of initial planning as essential for a productive engagement.

2. Recon Prompts, Plugins, and Retrieval Connectors

Next, catalog potential interfaces. This includes system and user prompts, integrated plugins, and any retrieval or retrieval augmented generation (RAG) pipelines. The goal is to understand every path through which data and commands flow. It is recommended to treat each connector as its own threat surface.

3. Exploit with Curated Prompts and Fuzzers

With the scope defined, you should now craft exploit inputs. This step includes the use of fuzzing tools, which are automated systems that generate randomized or mutated prompts, to test how the model responds to unexpected inputs. In AI red teaming, fuzzing simulates real-world adversarial behavior by probing edge cases that may trigger unsafe outputs. Tools such as RedTeamLLM, discussed in this report, automate multi-step, LLM-agent workflows and uncover when controls break down. This stage identifies instances where the model fails to enforce safety policies or exhibits misbehavior.

4. Document Impact and Leaked Snippets

After exploitation, it’s critical to record outputs systematically. Capture leaked snippets, failure phrases, and response logs. Assess severity: Does it expose PII? Does it enable unauthorized API usage? Documentation must be replicable and precise for developers and auditors, as clear documentation fuels iterative hardening.

5. Remediate with Policy and Guardrails

Next, enforce corrections. Add prompt-level filters, adjust retrieval logic, refine vector stores, and implement plugin access controls. Deploy static policies or dynamic knowledge guardrails that block or redact unsafe outputs. Each guardrail should be mapped to a specific failure mode, ensuring that mitigations are precise, targeted, and enforceable.

6. Retest to Confirm Fixes

Finally, loop back to verify your fixes. Test exploits against the updated system and validate that leaks no longer occur. Verify that misused APIs no longer respond. Manual red teaming followed by systematic re‑measurement and re‑validation is the essence of this phase.

How Knostic Makes Red Team Results Stick

Knostic delivers AI red teaming through its Copilot Readiness Assessment Tool (CRAST), and then transforms those red-team findings into operational governance. Instead of relying on static keyword tagging, CRAST builds a unified knowledge graph that maps how users, content, and roles interact across real workflows. This includes permission and access mapping that pulls from directory services and repositories, showing file permissions, group memberships, and inherited access to uncover misconfigured sharing.

CRAST also provides comprehensive oversharing discovery, detecting documents, conversations, and indexes exposed across repositories. It performs persona-based analysis to evaluate access against least-privilege policies. Its AI-aware risk mapping highlights how overshared content could be incorporated into AI training or retrieval systems. In contrast, prioritized risk scoring ranks issues by business impact, allowing teams to know where to focus remediation first.

The platform continuously simulates LLM usage to detect exposure drift when models update, permissions shift, or content changes, ensuring that violations do not silently reappear. In RAG pipelines, it analyzes both prompts and retrieved content, flagging violations when combinations of context and inference break policy. All insights feed back into governance tools, such as Microsoft Purview or internal DLP systems, providing actionable remediation guidance, including revoking links, tightening group memberships, or applying sensitivity labels. This way, Knostic ensures that red team results translate directly into lasting improvements without disrupting user experience.

What’s Next

The best way to understand LLM exposure is to simulate it. Knostic offers a demo interface that lets you explore how LLMs might overshare information across tools like Copilot and Glean. You can see how oversharing is detected and remediated, all in a secure, controlled environment. Test for yourself:  https://prompts.knostic.ai/ 

FAQ

• How is AI red teaming different from classic app red teaming?

Traditional red teaming targets network ports, API calls, or static configurations. AI red teaming targets language patterns, retrieval logic, and inferential reasoning. It exploits what the model says, not what it executes.

• What KPIs show red-team success?

Metrics include attack success rate, number of policy violations caught, percentage of redacted unsafe responses, and mean time to remediation. 

• How often should we retest after each model update?

Every major model update or permission change introduces new risks. Knostic enables continuous simulation of AI interactions to detect exposure drift after model or system changes.