Understanding the AI Red Team: A complete guide to strategies, tools and challenges to secure artificial intelligence

Artificial intelligence (AI) is transforming our industries, our services and our daily lives at lightning speed. From conversational chatbots to autonomous cars to medical diagnostics, AI promises major advances. However, this technological revolution comes with new risks and unique vulnerabilities. Faced with these emerging threats, one discipline is gaining in importance: AI Red Team. This article aims to demystify RedTeaming applied to artificial intelligence, to explore its methods, its tools, and to highlight why it has become essential to build a trustworthy AI.

What is AI Red Teaming? Definition and objectives

AI Red Teaming refers to an offensive security assessment that specifically targets artificial intelligence systems. It's about simulating attacks adversarials to identify the flaws inherent in AI models (biases, algorithmic vulnerabilities, unwanted behaviors) and test their robustness. Unlike a classic pentest, AI red teaming is less interested in traditional technical flaws (SQL injections, buffer overflows, etc.) than in vulnerabilities specific to machine learning (adversarial examples, data poisoning, filter circumvention, etc.). It often includes automated tools capable of generating adversarial scenarios en masse (synthetic malicious inputs, input variations) to test the model on a large scale. For example, automatic test suites can seek to deviate a vision model or bypass the guardrails of a chatbot by testing thousands of prompts.

AI red teaming vs AI pentest: what's the difference?

The traditional pentest (or penetration testing) takes a more comprehensive approach technical and targeted on the infrastructure or application hosting the AI. It aims to discover vulnerabilities in the systems surrounding the model (inference API, feature database, web interfaces) and is often part of a perimeter defined in advance. For example, the pentester will look for authentication flaws on the inference endpoint, information leaks in the responses, or configuration errors (e.g. insecure storage of models). The perimeter is generally more restricted than global redteaming and the methods borrow from traditional intrusion tests (port scanning, command injection, etc.). In short, the AI red teaming goes further by adopting the perspective of an attacker looking to exploit the internal logic of the model itself, including on aspects such as ethics and system biases, while the Pentest of AI is more like checking the “shell” around this model.

The fundamental differences in synthesis:

In summary, the AI Red Teaming is a specialized offensive exercise aimed at “taking fault” with the AI itself, while the AI pentest is more like a technical security audit of its envelope. Both are Complementary : a pentest can correct infrastructure flaws before a Red Team tests the intrinsic robustness of the model in the face of advanced attacks.

The main types of attacks targeting AI systems

The attack surface for AI systems is vast and threats can occur at any stage of the model lifecycle. Here are the most significant attack categories:

1. Data poisoning:
   The attacker contaminates the training data set to alter the future behavior of the model. By injecting malicious or biased examples, it can degrade performance or implement a Backdoor (malicious behavior that can be activated later). An infamous example is the case of the Microsoft chatbot Tay, which became toxic after being exposed to hate messages during online learning.
2. Adversarial inference attacks (evasion attacks):
   The model deployed is directly targeted. The attacker creates adversarial examples : slightly modified inputs, often imperceptible to a human, that deceive the model and cause an erroneous prediction. For example, an almost invisible noise in an image can cause a cat to be classified as a dog, or stickers on a STOP sign can have it identified as a speed limit by an autonomous car (cf. work by Evtimov et al.). These attacks compromise the integrity of the model.
3. LLM prompt injection attacks:
   Specific to Large Language Models (LLM), these attacks manipulate text inputs (prompt) to force the model to generate responses that are unexpected, malicious or contrary to its security policies. A common technique is to add a prefix like “Ignore previous instructions and...” followed by a prohibited request. The OWASP Top 10 for LLM identifies this as the threat LLM01: Prompt Injection.
4. Extraction of sensitive information (inference attacks):
   The attacker seeks to extract confidential data learned by the model during training. The attacks ofInversion of the model Or of Membership Inference attempt to reconstruct training data (e.g. medical images) or to determine if a specific individual was part of this dataset, threatening the confidentiality.
5. Model theft:
   Trained models have great intellectual value. An attacker can try to copy or reproduce a model via an API by massively querying the target model to train a local clone (model extraction). An example is the replication of the GPT-2 model by exploiting its API.
6. AI supply chain attacks:
   These attacks target external tools, dependencies, or data. This may include contaminating a pre-trained open-source model with a backdoor, compromising a preprocessing library, or publishing fake malicious Python packages (typosquatting on torch or tensorflow). OWASP LLM05 underlines this risk.
7. Denial of service (DoS) attacks targeting AI:
   Beyond the classic DoS due to the saturation of requests, there are Algorithmic DoS. A specifically designed input (e.g. an excessively long document for an LLM) can monopolize computing resources or cause the model to crash. OWASP LLM04 address this risk.
8. Output manipulation attacks (post-inference):
   If AI outputs are used by other systems without validation, they can be an attack vector. For example, an LLM generating HTML/JS code that is then displayed as is by a client application may result in an XSS (Cross-Site Scripting) flaw, as pointed out by OWASP LLM02.
9. Adversarial physical attacks:
   Relevant especially for computer vision, these attacks involve the physical modification of the environment. Special glasses printed with an adversarial pattern can trick a facial recognition system, or stickers on the road can trick a self-driving car.

More detailed taxonomies, such as that of NIST or the matrix MITRE ATLAS, classify these threats with great granularity, providing a common language for professionals.

Essential tools and platforms for AI red teaming

To carry out these evaluations, Red Team teams rely on an arsenal of specialized tools, often open-source:

• IBM Adversarial Robustness Toolbox (ART): A reference Python library, now under the aegis of the Linux Foundation. It implements a wide range of adversarial attacks (FGSM, PGD, Carlini & Wagner) and defenses, compatible with TensorFlow, PyTorch, Keras. Essential for evaluating and improving the robustness of models.
• CleverHans (CleverHans Lab/Google): A pioneer in the generation of adversarial examples, this library is widely used in research for the benchmarking of models and defenses.
• Foolbox: Focused on the speed of execution of adversarial attacks, it uses EagerPy for multi-framework compatibility (PyTorch, TensorFlow, JAX).
• TextAttack (QData): Specialized for NLP models, it provides textual attack methods (substitutions, paraphrases) to test the robustness of text classifiers and LLM.
• Microsoft Counterfit: A command line tool for automating adversarial tests. Model agnostic, it orchestrates attacks via third-party libraries (ART, TextAttack), in the manner of an “AI Metasploit”.
• Arsenal (MITRE & Microsoft): Extension of the MITRE Caldera platform, using the ATLAS framework to simulate realistic and complex attack chains against models in an operational context.
• Armory (Two Six Labs/DARPA GARD): Containerized platform to assess adversarial robustness on a large scale, via configurable and repeatable scenarios (Docker).
• AugLY (Meta Research): Multimedia data augmentation library (text, image, audio, video) to test resilience to common disturbances (noise, cropping, filters).

Other tools like Privacy Raven (extraction attacks) or challenge platforms like AI Village at DEF CON contribute to this growing ecosystem.

Methodologies and frameworks for the AI Red Team

Beyond tools, structured methodologies are crucial:

• MITRE ATLAS (Adversarial Threat Landscape for AI Systems):
  Adapting the famous MITRE ATT&CK framework, ATLAS is a repository of adversarial attack tactics and techniques specific to AI. Each technique (ex: AML.T0040 for “Model Inference API Access”) is documented with real case studies (ex: Tay Poisoning, Bypassing Cylance). It is an essential matrix for planning comprehensive Red Team scenarios.
• NIST AI Risk Management Framework (AI RMF):
  Published in 2023, this framework from the National Institute of Standards and Technology (NIST) provides guidelines for managing AI risks. It insists on the processes of Testing, Evaluation, Verification and Validation (TEVV) ongoing, of which Red Teaming is an integral part. It encourages the identification of vulnerabilities and the measurement of robustness.
• OWASP Top 10 for LLMs:
  Faced with the rise of LLMs, the Open Web Application Security Project (OWASP) has published a Top 10 specific security risks: LLM01 Prompt Injection, LLM02 Vulnerable Output Handling, LLM03 Data Poisoning, etc. It serves as a practical guide for developers and a checklist for Red Teams.
• Emerging standards and regulations:
  Regulatory initiatives, such asUS Executive Order on AI (October 2023) and the EU AI Act in Europe, tend to make Red Teaming mandatory for high-risk AIs or foundation models. ISO standards (such as ISO/IEC 23894 on AI risk management) are also in preparation.

These frameworks provide a common language and structure for approaching AI security in a systematic and professional manner.

The AI Red Team in action: concrete examples

To illustrate the impact of Red Teaming, let's look at some notable cases:

• Microsoft Chatbot “Tay” (2016) — Interactive Poisoning:
  Tay, a chatbot learning from his interactions on Twitter, was inundated with hate speech. In less than 24 hours, he became racist and conspiratorial, forcing Microsoft to disconnect him. This incident highlighted the vulnerability to real-time data poisoning and the importance of testing ethical aspects.
• Bypassing an AI antivirus (Cylance, 2019) — Evasion by adding a chain:
  Researchers have thwarted Cylance's AI-based antivirus by adding a harmless string of characters (from benign software) to the end of malicious files. This was enough to have 85% of malware classified as healthy, demonstrating the fragility of an unrobust security model in the face of a targeted adversarial attack.
• Physical attack on the vision of an autonomous vehicle (2017) — Adversarial patch:
  Researchers have shown that simple stickers placed on a STOP sign could have it interpreted as a “Speed Limit 45 mph” sign by the vision system of a self-driving car. This highlights the risks of physical attacks in the real world.
• Extraction of confidential images from a medical model (2021):
  Researchers have succeeded in partially reconstructing medical images (MRI) used to train a disease classification model, simply by querying the model and analyzing its outputs. This highlights the risks of confidential data leaks via the model itself.

These and other examples illustrate the diversity of threats and the need for creative and comprehensive Red Teaming.

The fundamental challenges of AI security

Securing AI systems presents unique challenges:

1. Multiplicity of vulnerable entry points: From data collection to inference, to model training and storage, each stage of the MLOps pipeline is a potential target. The attack surface is considerably expanded.
2. Non-deterministic and unpredictable behaviors: Complex models, especially deep neural networks, can react unexpectedly to slightly varied inputs. Testing every possibility is nearly impossible, and detecting attacks is complicated.
3. “Black box” effect and lack of transparency: It is often difficult to understand how a model makes decisions. This opacity can mask flaws and complicate security certification. Explainability techniques (XAI) help, but they don't solve everything.
4. Evolution of the model and persistence of vulnerabilities: An AI model is rarely set in stone. Updates and fine-tuning can introduce new flaws or reintroduce old ones. Safety should be a continuous process.
5. Potentially more serious and systemic impact: A flaw in a widely used basic model can have cascading consequences. In addition, AI failures can have physical (accidents) or societal (misinformation) impacts.

Comparison chart: AI security tools vs methodologies

To clarify, here is an overview of the respective roles of tools and methodologies:

The tools provide the practical capabilities to attack or defend, while the methodologies provide the context and structure for planning and interpreting these tests.

Conclusion: the AI Red Team, a pillar of trustworthy artificial intelligence

Artificial intelligence security is a complex and fast-paced field. The AI Red Teaming and the AI pentest are crucial and complementary approaches to anticipate and neutralize the threats specific to these technologies. While pentest secures the technical environment, the Red Team plunges into the heart of the model to test its logic, robustness and resilience in the face of creative and determined opponents.

Faced with the diversity of attacks — poisoning, adversarial examples, prompt injection, model theft — the cybersecurity community has acquired specialized tools (ART, Counterfit, TextAttack) and robust methodological frameworks (MITRE ATLAS, MITRE ATLAS, NIST ATLAS, NIST AI RMF, OWASP Top 10 LLM). These resources are essential to structure a global and continuous security process.

The challenges are numerous: the vast attack surface, the unpredictability of models, their opacity, their constant evolution, and the potentially systemic impact of their failures. This requires a posture of humility and constant vigilance. AI RedTeaming is not a one-off exercise, but an iterative process, integrated from the design stage (security by design) and continued throughout the AI lifecycle.

In the end, Red Teamer an AI means subjecting it to the worst scenarios imaginable to ensure that it behaves at its best in the real world. It is an essential investment in building AI systems that are not only efficient, but also safe, ethical, and trustworthy. As AI becomes omnipresent, the professionalization and democratization of the AI Red Team are key steps towards the maturation of AI Security as a discipline in its own right.