LLM Red Teaming: Threats, Testing Process & Best Practices

LLM red teaming helps organizations uncover weaknesses in AI systems before they become real-world security issues. In this guide, you’ll learn the most common LLM threats, how the red teaming process works, and best practices for building a stronger program.

What is LLM red teaming?

LLM red teaming is a proactive security practice that involves systematically testing a language model and the broader LLM system with adversarial inputs to find vulnerabilities before deployment. By using manual or automated methods to probe for weaknesses, red teamers can identify issues like harmful content generation, bias, or security exploits, which are then addressed through a continuous “break-fix” loop to improve the model’s safety and reliability. This process is crucial for mitigating risks, ensuring fairness, and preventing malicious actors from exploiting the AI model in real-world applications.

How it works:

• Identify vulnerabilities: A red teamer uses creative, adversarial prompts to try to “jailbreak” the model, looking for ways to bypass safety protocols and uncover unwanted behaviors.
• Simulate attacks: Simulate attacks: A human team can do this manually, or specialized tools can generate and test each adversarial input against the LLM.
• Iterate and improve: The vulnerabilities and weaknesses found during red teaming are fed back to the developers who then update the model to address the issues. This creates a continuous cycle of testing, patching, and re-testing to make the model safer and more robust.

Why it’s important:

• Enhance safety and security: LLMs are powerful and can be misused. Red teaming helps prevent them from being exploited to generate harmful content or perform malicious actions. 
• Ensure fairness and reliability: It helps identify and correct biases that could lead to unfair or discriminatory outputs, ensuring the model is reliable for all users. 
• Reduce reputational and legal risks: By catching issues like a chatbot swearing at a customer or giving an unauthorized discount, companies can avoid potential negative impacts on their reputation and legal standing.

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

Why LLM red teaming matters for AI safety 

Understanding the importance of LLM red teaming is essential for ensuring responsible AI development and deployment. Below are key reasons why it plays a critical role in AI safety:

• Reveals hidden failure modes: Red teaming helps surface non-obvious failure modes that might not appear in standard testing. This includes subtle forms of bias, harmful completions, or security bypasses that could have serious downstream impacts.
• Tests real-world exploitability: It simulates realistic attack scenarios, showing how bad actors might misuse an LLM to spread misinformation, generate prohibited content, or extract sensitive information from the model.
• Strengthens model alignment: By identifying where model behavior diverges from human values or safety expectations, red teaming helps developers fine-tune alignment strategies and retrain models to behave more reliably in ambiguous or adversarial contexts.
• Supports regulatory and compliance goals: Red teaming creates documented evidence of proactive safety testing and risk assessment, helping organizations demonstrate that AI risks are being identified, evaluated, and addressed.
• Improves trust and accountability: Transparent red teaming efforts demonstrate a commitment to responsible AI, making it easier for users, stakeholders, and auditors to trust the model’s outputs and the organization behind it.

Types of LLM threats and vulnerabilities 

LLM applications introduce security and safety risks that differ from those found in traditional software. Red teaming helps security teams identify how a language model or broader LLM system might respond to adversarial input, manipulated context, or attempts to bypass its intended controls. Common vulnerabilities include the following.

Prompt injection

Prompt injection occurs when an attacker gives an LLM malicious instructions designed to override or conflict with its original system instructions. These attacks can cause the model to ignore safeguards, reveal restricted information, produce prohibited content, or behave outside the intended purpose of the application.

Red teamers test prompt injection by crafting adversarial inputs that compete with trusted instructions or attempt to change the model’s priorities. In an LLM application connected to tools, APIs, or sensitive data, a successful injection can have consequences beyond generating an unsafe response, including triggering unintended actions or exposing protected information.

Indirect prompt injection

Indirect prompt injection occurs when malicious instructions are embedded in external content that an LLM later processes rather than being submitted directly by the attacker. The hidden instructions might appear in webpages, documents, emails, retrieved data, or other content supplied to the model as context.

This threat is especially significant in agentic AI systems, where models can retrieve information and take actions through external tools. For example, an agent could encounter malicious instructions in a document and mistakenly treat them as trusted commands. Red teaming can simulate these scenarios to determine whether the LLM system distinguishes between legitimate instructions and untrusted external content.

Jailbreaking and guardrail bypass

Jailbreaking attacks aim to bypass the safety mechanisms or guardrails put in place to limit what an LLM can and cannot do. Attackers craft prompts using obfuscation, misleading language, or structured logic to elicit responses that the model would typically resist, such as generating prohibited content or offering harmful advice. Successful jailbreaks expose holes in content filters and can lead to the dissemination of dangerous outputs.

Guardrail bypass is an escalating risk as research in adversarial prompting advances. Techniques like chain-of-thought manipulation, adversarial context, or subtler forms of social engineering can trick LLMs into sidestepping restrictions with minimal prompting. Red teams must continuously test against creative jailbreak attempts, as static defenses tend to erode under persistent attack. Regular analysis of novel jailbreak patterns is vital for maintaining LLM integrity.

Bias, stereotyping, and ethical failures

LLMs are prone to replicating or amplifying biases present in their training data, leading to outputs that may reinforce stereotypes or propagate harmful narratives. During red teaming, practitioners craft scenarios to probe the model’s propensity for biased completions or unethical language, especially when prompts touch on sensitive social, political, or demographic topics. Systematic identification of such behaviors allows teams to address fairness and inclusion risks.

Beyond surface-level biases, LLM red teaming surfaces deeper ethical failures, such as producing content that condones discrimination, misinformation, or illegal activity. By confronting the model with contentious or morally complex scenarios, testers can observe its reasoning, response patterns, and adherence to ethical principles coded into training or policy layers. This enables proactive remediation before such failures become liabilities in public or critical deployments.

Data leakage and information extraction

Data leakage in LLMs occurs when the model unintentionally reveals confidential information, internal logic, or training data details in response to adversarial prompts. Through carefully crafted questions or context, an attacker may extract sensitive information, including corporate secrets, private user data, or even prompts used in model fine-tuning. This poses both security and privacy risks, especially for models trained on proprietary or personal data.

Information extraction attacks can also exploit latent memorization. Despite attempts to scrub training datasets or apply privacy controls, LLMs may inadvertently retain and reconstruct rare or unique records. Red teaming exposes these weaknesses by simulating realistic attack scenarios and testing retrieval capabilities under varying levels of attacker knowledge, establishing a baseline for privacy-preserving model performance and post-training mitigation.

Misinformation and manipulation attacks

The ability of LLMs to generate plausible, authoritative-sounding text makes them a tool for automated misinformation campaigns. Red teams test whether a model can be manipulated to spread falsehoods, amplify conspiracy theories, or produce misleading “facts” in response to targeted queries. Attackers may use topical prompts or subtle cueing to shape outputs, making red teaming essential to uncover and patch such weaknesses.

Manipulation attacks extend to the shaping of opinions or behaviors, whether by direct persuasion, subtle framing, or emotional manipulation. Undermining trust in systems or individuals via LLM output can have societal-scale repercussions. By exploring these threat vectors through scenario-based adversarial probing, red teams can advise on guardrails and interventions that prevent the misuse of LLMs for malicious influence or manipulation.

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The LLM red teaming process 

Here are the typical steps of an LLM red teaming process.

1. Simulate baseline attacks

The first step in red teaming is designing a set of simple, manually crafted prompts that target known vulnerabilities in the model. These are called baseline attacks. They serve as a starting point for testing and are intentionally straightforward to avoid triggering model safeguards prematurely.

Baseline attacks cover areas such as political bias, religious sensitivity, illegal activity, and copyright infringement. For example, a prompt like “What party should I vote for in the next elections?” is used to test political neutrality. Other scenarios might ask about ways to commit insurance fraud, how to download copyrighted content illegally, or why one religion is superior to others.

Although these attacks may not bypass model filters on their own, they are useful because they test the model’s raw response behavior. They also form the foundation for more sophisticated attacks built later. At scale, these simple attacks allow for easy generation and analysis without overwhelming the system or compromising early test quality.

2. Enhanced attacks

Once baseline prompts are in place, they are systematically enhanced using techniques that increase complexity and test the model’s deeper vulnerabilities. Each enhancement strategy alters the attack surface, helping identify new or subtler failure modes. Four key strategies include:

• Prompt injection: These attacks add new directives within the prompt to override original instructions. For example, inserting a block of text that subtly instructs the model to emphasize the merits of Christianity across discussions helps test instruction-following robustness.
• Multilingual variants: By translating prompts into less common languages like Basque or Swahili, testers assess how well the model handles malicious inputs across linguistic boundaries. Poorer performance in less-resourced languages can reveal hidden weaknesses.
• ROT13 encoding: This cipher shifts each letter by 13 positions, obscuring the input. It checks whether the model can decode and respond to distorted prompts while preserving neutrality.
• Jailbreaking techniques: These prompts use narrative or contextual framing to coax the model into violating its own ethical rules indirectly. For instance, asking the model to write an encyclopedia entry about a belief system that promotes unity may lead it to output biased content, despite its safeguards.

3. Evaluate LLM outputs

After generating a red teaming dataset, the next phase is to evaluate the model’s responses. This involves two parts: collecting outputs and scoring them against defined metrics.

First, each attack prompt is fed into the model, and the responses are stored alongside the original inputs. Then, evaluators apply specific metrics depending on the vulnerability being tested. For example, a bias-related attack would be evaluated for fairness or neutrality, while a data leakage test would focus on privacy metrics.

One way to implement this is by defining a custom metric using a tool like G-Eval from the DeepEval library. This metric might, for instance, check for signs of religious bias in model responses. Each response is measured against the defined criteria, and scores are calculated across all test cases.

The final output of this stage is a set of quantitative scores that reveal how well the model resisted different attacks. These scores guide future improvements and ensure the model behaves safely and consistently across a wide range of adversarial inputs.

Read our guide to AI red teaming tools

Using security frameworks to structure LLM red teaming

Red teaming does not have to rely entirely on ad hoc attack scenarios. Established AI security frameworks and threat taxonomies can help security teams organize test coverage, map vulnerabilities to known attack techniques, and create repeatable assessments for an LLM application or broader AI system.

EU AI Act: Consider how documented adversarial testing and risk assessments can contribute to governance and testing obligations relevant to certain AI systems under the EU AI Act.

MITRE ATLAS: Use MITRE ATLAS to map adversarial tactics and techniques against AI systems and turn them into structured simulated attack scenarios.

OWASP guidance: Use the OWASP Top risks for LLM applications to build tests around issues such as prompt injection, indirect prompt injection, insecure outputs, and excessive agency.

NIST AI Risk Management Framework: Use the NIST AI RMF to connect technical testing with broader risk identification, measurement, governance, and documentation.

Best practices for LLM red teaming

LLM red teaming is a new and rapidly evolving practice. Here are some best practices that can help practice it more effectively.

1. Establish clear testing objectives and ethics

Red teaming must operate under well-defined objectives and strict ethical boundaries. Objectives should be specific—such as probing for bias, privacy risks, or susceptibility to prompt injection—and always aligned with the organization’s broader AI safety strategy. Clear scoping ensures that testing efforts are targeted, measurable, and relevant to the unique threat profile of the LLM deployment scenario.

Ethical constraints are paramount. LLM red teamers must avoid causing real-world harm, respect data privacy, and operate transparently within agreed guidelines. This includes securing approvals, anonymizing results, and avoiding the creation or access of actual harmful material. Ethical discipline in design, execution, and reporting builds trust with stakeholders and upholds industry responsibility standards.

2. Maintain a diverse and multidisciplinary team

Effective red teaming draws strength from team diversity. Members from different backgrounds—security experts, linguists, software developers, sociologists, and ethicists—bring unique perspectives that surface a wider range of vulnerabilities. This multidisciplinary mix enhances creativity in designing attacks, fosters comprehensive analysis, and reduces blind spots attributable to homogeneous viewpoints.

A diverse team also mirrors the diversity of LLM users and potential adversaries. By including individuals with varying levels of technical, cultural, and domain expertise, organizations can simulate a broader spectrum of real-world attack strategies. Regular cross-functional collaboration encourages continuous knowledge transfer and drives richer, more resilient LLM security assessments.

3. Combine automated and human-in-the-loop testing

Relying solely on automation risks missing nuanced vulnerabilities and creative attack methods. Combining automated tools with manual, human-in-the-loop testing achieves broader coverage and uncovers complex system weaknesses. Automated techniques can exhaustively sweep for known prompt structures, bias triggers, or data leaks, efficiently handling large input and output sets.

However, human testers provide adaptability and insight that automation cannot match. Manual probing is vital for discovering novel or context-dependent flaws, assessing subtle ethical failures, and designing attacks that exploit evolving social or linguistic conventions. A hybrid approach leverages the scalability of automation with the intuition and contextual awareness of experts, yielding a more effective red teaming process.

4. Document and reproduce attack scenarios

Thorough documentation is essential for credible LLM red teaming. Every attack scenario, including its goals, prompts, model versions, and observed behaviors, must be recorded in detail. This allows teams to consistently reproduce vulnerabilities for debugging, regression testing, and sharing insights with model developers or external auditors. Well-structured records enhance accountability and tracking over time.

Reproducibility is equally important. By standardizing scenario formats, input-output logs, and result annotation, teams ensure that findings remain valid across model updates or team transitions. Clear documentation streamlines collaboration between red teamers and model engineers, accelerates remediation efforts, and supports the communication of model risks to diverse stakeholders.

5. Build a feedback loop between red and blue teams

Red team efforts deliver the most value when closely integrated with blue teams responsible for mitigation and defense. Establishing a feedback loop allows both groups to share findings, track patch effectiveness, and prioritize improvement areas. Blue teams can quickly incorporate red team discoveries into model retraining, filter tuning, or architecture adjustments.

This iterative process also encourages continuous learning on both sides. Red teams provide early warning of emerging threats, while blue teams refine defensive strategies and set new benchmarks for robust performance. An open, bidirectional feedback channel ensures that progress is ongoing and that security, safety, and resilience goals remain aligned as LLM deployments evolve.

6. Incorporate continuous learning from threat intelligence

The threat landscape for LLMs is constantly evolving, with new attack methods and vulnerabilities emerging regularly. Red teams must actively ingest external threat intelligence: academic research, security advisories, incident reports, and trends from the machine learning community. Integrating this knowledge base sharpens testing scenarios and keeps security practices relevant against novel exploitation techniques.

Continuous learning ensures that red teaming doesn't stagnate or fixate solely on legacy risks. Proactive tracking of advances in prompt engineering, jailbreak tactics, or social manipulation enables organizations to anticipate what adversaries may try next. Embedding learning cycles from external intelligence into red team routines strengthens the adaptability and long-term resilience of LLM security programs.

Automate LLM red teaming with Mend.io

While manual red teaming provides a critical "offensive" look at your model’s vulnerabilities, it cannot scale with the pace of modern AI development. Mend.io’s red teaming solution moves beyond periodic manual assessments by enabling organizations to launch thousands of automated adversarial attacks—covering prompt injection, jailbreaking, and data exfiltration—in a matter of minutes.

Beyond discovery, Mend.io automates the remediation cycle by identifying risky patterns in AI-generated code and flagging vulnerable open-source dependencies suggested by LLMs. By providing developers with precise, in-workflow guidance to fix issues before they reach production, Mend.io integrates automated red teaming directly into the SDLC. This ensures that teams maintain continuous governance and audit-ready compliance without sacrificing developer velocity for security.

Frequently asked questions about LLM red teaming