Table Of Content
As artificial intelligence rapidly evolves, Large Language Models (LLMs) have emerged as powerful tools, transforming industries from customer service to content generation. However, with great power comes great responsibility. Just as the internet once introduced new risks that required vigilant cybersecurity practices, the widespread adoption of LLMs has ushered in a new era of security challenges.
OWASP LLM Top Ten Primer
The OWASP LLM Top 10 is an evolving project to identify and rank the most critical security risks associated with Large Language Models (LLMs). Given the increasing adoption of LLMs in various applications, understanding these risks is crucial for developers, security professionals, and organizations integrating LLMs into their systems.
According to the OWASP LLM Top Ten Project, the project aims to educate developers, designers, architects, managers, and organizations about the potential security risks when deploying and managing large language models (LLMs). The goal is to raise awareness of these vulnerabilities, suggest remediation strategies, and ultimately improve the security posture of LLM applications.
According to the OWASP LLM Top Ten Project, the project aims to educate developers, designers, architects, managers, and organizations about the potential security risks when deploying and managing large language models (LLMs). The goal is to raise awareness of these vulnerabilities, suggest remediation strategies, and ultimately improve the security posture of LLM applications.
High-Level Overview for LLM Top Ten (v1)
Like other OWASP Top Ten projects, OWASP LLM Top 10 follows the same format, focusing on security issues that can lead to vulnerabilities in LLM applications.
1. Prompt Injection Attacks:
- Description: Malicious users craft inputs that can manipulate the model’s behavior, leading to unintended actions or information leakage.
- Example: An attacker inserts commands into user inputs that cause the LLM to execute unexpected tasks.
2. Data Leakage:
- Description: The model inadvertently generates Sensitive or proprietary information, potentially due to training on inadequately filtered data.
- Example: The model might reveal confidential business strategies or personal data.
3. Model Bias:
- Description: The LLM exhibits biased behavior due to skewed training data, leading to unfair or discriminatory outcomes.
- Example: The model may generate biased content based on race, gender, or other characteristics.
4. Training Data Poisoning:
- Description: An adversary intentionally inserts malicious data into the training dataset, causing the model to learn and replicate harmful behaviors.
- Example: Introducing toxic content into training data induces the LLM to produce harmful outputs.
5. Insecure Model APIs:
- Description: Vulnerabilities in the API interfaces of LLMs could be exploited to perform unauthorized actions or extract sensitive data.
- Example: An attacker could use API calls to extract parts of the model’s training data.
6. Model Theft:
- Description: Unauthorized access to and exfiltration of a trained LLM model, leading to intellectual property theft or competitive disadvantage.
- Example: An adversary copying and using a proprietary model for their purposes.
7. Inadequate User Input Validation:
- Description: Failing to properly sanitize and validate user inputs before processing them with an LLM, potentially leading to injection attacks or other security issues.
- Example: Allowing unrestricted user inputs that cause the model to misbehave or leak information.
8. Misuse of Generated Content:
- Description: Content generated by LLMs can be misused for malicious purposes, such as generating fake news, phishing, or other harmful content.
- Example: Using LLMs to generate convincing spear-phishing emails.
9. Model Overfitting and Over-Confidence:
- Description: A model that is too confident in its predictions, even when wrong, can lead to dangerous assumptions in critical applications.
- Example: An LLM confidently providing incorrect medical advice.
10. Privacy Violations:
- Description: LLMs might inadvertently generate outputs that violate user privacy, especially if the model can access sensitive data.
- Example: Producing content that reveals personal details about individuals.
OWASP LLM Top Ten v2 (Refresh)
Since its inception in May 2023, the OWASP Top 10 for LLM Applications Project has experienced unprecedented growth, swiftly becoming a cornerstone in cybersecurity for AI applications. With its first release in August and a significant update in October, the Top 10 list has been heralded across the industry, earning citations from government agencies, standards bodies, academic circles, and businesses of every size. Building on this momentum, we introduced the LLM AI Cybersecurity & Governance Checklist, broadening our impact to directly engage with CISOs and compliance officers.
Incorporating feedback from our initial brainstorming, this roadmap sets the stage for a series of generation, voting, and intensive analysis phases, each designed to enrich our understanding and guidance on LLM vulnerabilities. Here’s a glance at our planned timeline:
Incorporating feedback from our initial brainstorming, this roadmap sets the stage for a series of generation, voting, and intensive analysis phases, each designed to enrich our understanding and guidance on LLM vulnerabilities. Here’s a glance at our planned timeline:
- April 15 – April 30, 2024: Voting on existing entries
- May 1 – May 8, 2024: Collation and publication of voting results
- May 15 – June 15, 2024: Call for new entries
- June 16 – June 30, 2024: Entry Voting
- July 1 – July 15, 2024: Voting collation and publication
- July 15 – Aug 1, 2024: Merging & Down selection
- Aug 1 – Sep 1, 2024: Data Analysis & Voting for Ranking
- Sep 1 – Sep 15, 2024: Entry Clean Up
- Sep 15 – Sep 30, 2024: Layout and pre-announcement work
- Oct 1, 2024: Publish V2
