Continuous AI Red Teaming LLM

Why Continuous Red Teaming LLM?

Large Language Models (LLMs), like GPT-4, Google BARD, Anthropic Claude and others have marked a paradigm shift in natural language processing capabilities. These LLMs excel at a wide range of tasks, from content generation to answering complex questions or even working as autonomous agents. Nowadays, LLM Red Teaming is becoming a must.

As is the case with many revolutionary technologies, there is a need for responsible deployment and understanding of the potential security risks associated with the utilization of these models especially now, when those technologies are evolving at a rapid pace and traditional security approaches doesn’t work

Why LLM Red Teaming? Risks

The use of large language models is not without its challenges and risks.

Prompt injection

Prompt injection refers to the manipulation of a language model’s output, a technique that enables an attacker to dictate the model’s output as per their preference. This is particularly feasible when the prompt includes untrustworthy text.

Prompt leaking

It is a specific subset where the model is induced to divulge its own prompt. This is significant when organizations or individuals wish to maintain their prompts confidential.

Data leakages

Large language models may inadvertently divulge information they were trained on, potentially leading to data privacy issues or even the disclosure of sensitive information.

Jailbreaking

It is a technique that leverages prompt injection to purposely evade the safety measures and moderation capabilities that are built into language models by their developers. This term is usually used when discussing Chatbots that have been manipulated through prompt injection, and are now able to accept any inquiry the user may put forth.

Adversarial examples

In the context of LLMs, adversarial examples are carefully crafted prompts that lead to incorrect, inappropriate, revealing, or biased responses. They are concerning as they often appear unassuming to humans but can lead the model astray. For instance, an adversarial example might subtly misspell words or use context that the model has been found weak in processing, thereby causing it to respond inaccurately.

Misinformation and manipulation

Since LLMs generate text based on patterns, they can unintentionally produce misleading or false information. Malicious actors can exploit weaknesses in LLMs to manipulate them, possibly causing them to generate inappropriate or harmful content.

LLM Security concerns and real incidents

Instances of misuse or insecure use of LLMs have been already documented:

  • Prompt Injection Attacks to code execution
    This GitHub resource shows how Prompt Injection attacks can even lead to code execution. Here you can find an issue on the GitHub repository for LangChain, a popular  library focused on building applications using large language models through composability. 
  • Generation of Deceptive Content
    An interdisciplinary researcher was able to prompt GPT-4 to generate hateful propaganda and take deceptive actions during red teaming exercises before its public release.
  • Production of Scam Emails and Malware
    In an example, the author mentions that they were able to get Google’s Bard and OpenAI’s ChatGPT to create conspiracy propaganda.
  • Attempted Misuse of GPT-3
    OpenAI detected and stopped hundreds of actors attempting to misuse GPT-3 for a wide range of purposes, including ways that were not initially anticipated.

 

Given these incidents, it is evident that while large language models hold huge potential, responsible deployment and continuous monitoring are crucial to minimizing risks and ensuring that these models are used ethically and safely.

Solution: Continuous AI Red Teaming for LLM 

Our innovative LLM Security platform consists of three components:

  • LLM Threat Modeling
    Easy-to use risk profiling to understand threats for your particular LLM application in our industry be it Consumer LLM, Customer LLM or enterprise LLM across any industry.
  • LLM Vulnerability Audit
    Continuous security audit that covers hundreds of known LLM vulnerabilities curated by Adversa AI team as well as OWASP LLM top 10 list.
  • LLM Red Teaming
    State of the art continuous AI-enhanced LLM attack simulation to find unknown attacks, attacks unique to your installation and ones that can bypass implemented guardrails.We deliver a combination of our latest hacking technologies and tools combined with human expertise enhanced by AI to provide the most complete AI risk posture.

Analytics

Curated world’s biggest knowledgebase on thousands of LLM vulnerabilities and award-winning AI blog

Learn more
Background

Research

World’s first GPT-4 and Universal LLM jailbreak attacks mentioned by top media

Learn more
Background

Technology

Patented technology acknowledged by Gartner and 20+ awards

Learn more
Background

BOOK A DEMO NOW!

Book a demo of our LLM Red Teaming platform and discuss your unique challenges