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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
BOOK A CALLGet Red Teaming experts to audit your LLM apps
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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 LLM systems.
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 LLM systems.
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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
Robustness
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
Scan
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.
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Report
Access a detailed vulnerability assessment of the LLM system, and educate your ML team about its major risks . Prioritize vulnerabilities based on business context.
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Mitigate
Review and implement suggested remediation strategies for your LLM application. Improve and compare application version performances in Giskard’s LLM Hub.
Deploy
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.
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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 AI security startup.
Creators of one of the most popular open-source LLM vulnerability scanning library on GitHub.
Identified as one of France’s Top AI security startup.
Creators of one of the most popular open-source LLM vulnerability scanning library on GitHub.
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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.