See what the OWASP Foundation's top 10 vulnerabilities are for large language models.

 

The AI explosion, fueled by large language models (LLMs), has sparked both excitement and concern. To address potential risks, OWASP Foundation has outlined the top 10 vulnerabilities facing LLMs.

Let's break them down:

  1. Prompt Injection: Attackers can manipulate LLMs through carefully designed prompts. This can lead to actions outside the model's intended purpose, like generating phishing emails or accidentally revealing sensitive information.
  2. Insecure Output Handling: LLM outputs must be sanitized before being fed into other systems. Failure to do so opens vulnerabilities like cross-site scripting (XSS) or request forgery (CSRF) attacks.
  3. Training Data Poisoning: LLMs are only as good as their training data. Malicious actors can inject harmful information during training, leading to biased outputs, security risks, or even copyright infringement.
  4. Model Denial of Service: Attacks designed to overload LLM resources can disrupt service for legitimate users or result in huge costs for the organization operating the model.
  5. Supply Chain Vulnerabilities: LLM developers, like any tech company, rely on third-party vendors. Vulnerabilities in these dependencies, like compromised training data, can weaken the core LLM itself.
  6. Sensitive Information Disclosure: User inputs can sometimes become a part of future LLM training. It's essential to be careful about sharing private information with models, as it might be unintentionally revealed later.
  7. Insecure Plugin Design: Plugins extend LLM functionality, but poor security practices can leave them open to exploitation. This can lead to privilege escalation or data leakage.
  8. Excessive Agency: When an LLM-based tool has too much power or autonomy, it can make harmful decisions or take unintended actions.
  9. Overreliance: LLMs can generate believable but inaccurate information. Overtrusting these models can result in everything from a failing grade on a history paper to vulnerable code in an application.
  10. Model Theft: Powerful, expensive LLMs are lucrative targets for theft. Compromised models can hurt their creators through lost revenue, reputational damage, or uncontrolled use that causes harm.

Responsible AI Development

LLMs hold great potential, but addressing these vulnerabilities is key to building trustworthy and secure AI systems. Organizations and users alike need to play their part in mitigating these risks.