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February 29, 2024
4 min read

LLM Red Teaming: Detect safety & security breaches in your LLM apps

Introducing our LLM Red Teaming service, designed to enhance the safety and security of your LLM applications. Discover how our team of ML Researchers uses red teaming techniques to identify and address LLM vulnerabilities. Our new service focuses on mitigating risks like misinformation and data leaks by developing comprehensive threat models.

Giskard's LLM Red Teaming
Blanca Rivera Campos
Giskard's LLM Red Teaming
Giskard's LLM Red Teaming

Hi there,

The Giskard team hopes you're having a good week! This month we have the pleasure to introduce LLM Red Teaming, to help you detect safety and security breaches in your LLM apps.

This new service is possible thanks to our great team of ML Researchers specialized in LLM Safety, who has an extensive knowledge of red teaming techniques from cybersecurity. To detect LLM vulnerabilities, they will develop comprehensive threat models with real attack scenarios.

As a company that advocates for responsible AI, we acknowledge the safety risks involved in language models. It is crucial to have independent third-party evaluations to audit your LLM applications. These evaluations, conducted by separate entities from the developers of LLMs, provide important checks and balances to ensure responsible regulation of the system.

We are happy to offer this new service to our valued users. 🫶 If you want to know of how can you assess your LLM apps, get in touch with our team!

Why Red Team LLMs?

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.

'LLM 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.

AI Incidents in the news

Put the security & reputation of your company & customers first

Our Red Teaming experts help you to protect your organization from critical LLM risks, such as:

✅ Hallucination & misinformation

✅ Harmful content generation

✅ Prompt injection

✅ Information disclosure

✅ Robustness issues

✅ Stereotypes & discrimination

How our Red Team can work with you

To detect and mitigate vulnerabilities in your LLM apps, our team will assist you to incorporate real attack scenarios and automate the security of your LLM systems. This will allow you to scale your security efforts for Generative AI.

⚡️ 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.

📊 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.

🛡️ 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.

Secure & Enterprise-Ready LLM Red Teaming

To operate in highly secure & compliant environments, our service allows for:

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.

RAG LLM system

Aligned with leading AI Security & Quality Standards

We align to top-tier frameworks and standards like MITRE ATLAS, OWASP, AI Vulnerability Database, and National Institute of Standards and Technology (NIST) 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 - International Organization for Standardization, at a global level.

👋 Meet our ML Researchers specialized in Red Teaming LLMs

Giskard's LLM Red Team

Find out more about our team's contributions to the open-source AI community:

To asses the security of your LLM applications:

👉 Get in touch with our team

🗺️ More to come

Our team is already working on the next features for our open-source library... 👀

Stay tuned for the latest updates!

Thank you so much, and see you soon! ❤️

The Giskard Team 🐢

Integrate | Scan | Test | Automate

Giskard: Testing & evaluation framework for LLMs and AI models

Automatic LLM testing
Protect agaisnt AI risks
Evaluate RAG applications
Ensure compliance