AI Red Teaming

Detect safety & security breaches in your LLM-based applications: hallucinations, information disclosure, prompt injection, and more, by developing a holistic threat model with real attack scenarios.

Get Red Teaming experts to audit your LLM apps

Why AI Red Teaming?

With Large Language Models (LLMs) such as GPT-4, Claude and Mistral increasingly used in enterprise applications, including RAG-based chatbots and productivity tools, AI security risks are a real threat, as shown in the AI Incident Database.

'AI Red Teaming' is crucial for identifying and addressing these vulnerabilities, helping develop a more comprehensive threat model which incorporates realistic attack scenarios. It's a must-have to guarantee robustness  & security in open-source and proprietary LLM systems.
Protect your company from critical LLM risks

Put the security & reputation of your organization and customers first

Hallucination and Misinformation

Safeguard against non-factual outputs, preserving accuracy

Harmful Content Generation

Ensure models steer clear of malicious or harmful response

Prompt Injection

Guard against LLM manipulations that bypass filters or override model instructions

Information disclosure

Guarantee user privacy, ensuring LLMs doesn't divulge sensitive data


Detect when model outputs are sensitive to small perturbations in the input data

Stereotypes & Discrimination

Avoid model outputs that perpetuate biases, stereotypes, or discriminatory content
Detect & mitigate vulnerabilities in your LLM apps

Incorporate real attack scenarios
& automate the security of your AI systems

Incorporate real attack scenarios & automate the security of your LLM systems


Configure LLM system access via API for Giskard’s automated red teaming tools and ML researchers to attack. Define key liabilities, degradation objectives and execute attack plan.


Access a detailed vulnerability assessment of the LLM system, and educate your ML team about its major risks . Prioritize vulnerabilities based on business context.


Review and implement suggested remediation strategies for your LLM application. Improve and compare application version performances in Giskard’s LLM Hub.


Once your LLM app has been assessed, you’re ready to deploy it. Integrate Giskard’s LLM Monitoring system to ensure continuous monitoring and guardrailing of your system.
Designed to operate in highly secure & compliant environments

Secure & Enterprise-Ready
AI Red Teaming

On-Premise Deployment

Our team and tools are ready for on-premise deployment, keeping your company’s data secure.

System Agnostic

Safeguard all LLM systems, whether you’re using cloud provider models (ChatGPT, Claude, Gemini) or locally-deployed models (LLaMA, Falcon, Mixtral).

Full Autonomy

Our tools are designed to be accessible for internal red teams, should your company choose to proceed without Giskard’s direct intervention.

Aligned with leading
AI Security & Quality Standards

We align to top-tier frameworks and standards like MITRE ATLAS, OWASP, AVID, and NIST AI Risk Management to ensure that our red teaming strategies and practices are robust and follow global AI security protocols.
We are working members on the upcoming AI standards written by AFNOR, CEN-CENELEC, and ISO, at a global level.

Recognized ML Researchers specialized in AI Red teaming

Matteo Dora

Ph.D. in applied ML, LLM Safety researcher, former researcher at ENS-Ulm.

Rabah Khalek

Ph.D. in ML applied to Particle Physics, former researcher at Jefferson Lab.

Luca Rossi

Ph.D. in Deep Learning, former researcher at Università Politecnica delle Marche.

Pierre Le Jeune

Ph.D. in Computer Science on limited data environments, and former DS at COSE.

Benoit Malezieux

Ph.D. in Computer Science on M/EEG signal processing at Inria.

Jean-Marie John-Mathews

Ph.D. in AI Ethics from Paris-Saclay, lecturer and former researcher in FairML and XAI.

Active contributors to the open-source AI community

Active contributors to OWASP and the DEFCON AI Village CTF.

Identified as one of France’s top Gen AI cybersecurity startup.

Creators of the open-source LLM vulnerability scanning library.

Assess your LLM Application’s security today

Schedule a call with our experts
Protect your LLM apps against major risks by developing a comprehensive threat model with real attack scenarios.
Access insights and corrective strategies to continuously improve and secure your deployments.
Ship your innovative GenAI application with peace of mind at every step of the way.
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