OWASP Top 10 for LLM

Language models have transformed the landscape of AI-driven applications, enabling powerful capabilities in natural language understanding, generation, and processing. However, with this advancement comes the critical responsibility of securing these Language Model Systems (LLMs) against potential vulnerabilities that could compromise their integrity, privacy, and functionality.

The Open Web Application Security Project (OWASP) identifies the top 10 vulnerabilities specific to LLMs. Let's explore each vulnerability in detail, along with common examples and preventive measures to fortify these systems.

1. Prompt Injections

Vulnerability:

Prompt Injection Vulnerabilities involve manipulating inputs cleverly, which can go undetected, leading to various manipulations. This vulnerability could result in data exposure or unauthorized actions, aligning with attackers' goals.

Common Vulnerabilities Examples:

Crafting input prompts to bypass security checks and gain unauthorized access.
Injecting prompts to alter model behavior and elicit unintended responses.

Prevention:

Implement strict input validation and sanitization techniques to filter out malicious prompts.
Employ context-aware checks to detect abnormal or unauthorized input patterns.

2. Insecure Output Handling

Vulnerability:

Occurs when plugins or apps accept LLM output without proper scrutiny, potentially leading to Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), Server-Side Request Forgery (SSRF), privilege escalation, remote code execution, and agent hijacking attacks.

Common Vulnerabilities Examples:

Allowing unfiltered output from the LLM to be directly displayed without proper encoding, leading to XSS vulnerabilities.
Accepting LLM output as commands without proper validation, leading to code execution.

Prevention:

Apply output encoding techniques to neutralize malicious scripts within the LLM output.
Validate and restrict the usage of LLM output to prevent unauthorized actions.

3. Training Data Poisoning

Vulnerability:

LLMs learn from diverse text sources, but they are susceptible to training data poisoning, resulting in misinformation to users. Overreliance on AI without vigilant oversight poses a significant risk.

Common Vulnerabilities Examples:

Introducing biased or manipulated data into the training dataset, affecting model behavior.
Exploiting vulnerabilities in data sources like Common Crawl or WebText to inject misleading information.

Prevention:

Implement robust data validation and cleaning mechanisms to detect and filter poisoned data.
Diversify training datasets and regularly audit sources for potential manipulation.

4. Denial of Service

Vulnerability:

An attacker interacts with an LLM in a resource-consuming manner, degrading service quality for users or incurring high resource costs.

Common Vulnerabilities Examples:

Flooding an LLM with excessive requests, causing it to become unresponsive or slow.
Exploiting inefficient algorithms or loopholes to exhaust system resources.

Prevention:

Implement rate limiting and throttling mechanisms to control incoming requests.
Optimize algorithms and system resources to handle unexpected spikes in traffic.

5. Supply Chain

Vulnerability:

LLM supply chains are prone to integrity issues, leading to biases, security breaches, or system failures due to vulnerabilities in pre-trained models, crowdsourced data, or plugin extensions.

Common Vulnerabilities Examples:

Compromising pre-trained models with backdoors or manipulated weights.
Injecting malicious code through unverified plugin extensions.

Prevention:

Conduct thorough security assessments and audits of pre-trained models and external data sources.
Enforce strict validation and signing mechanisms for plugin extensions to ensure authenticity.

6. Permission Issues

Vulnerability:

Lack of proper authorization tracking between LLM plugins can enable indirect prompt injection or malicious plugin usage, resulting in privilege escalation and confidentiality loss.

Common Vulnerabilities Examples:

Exploiting gaps in plugin communication to gain unauthorized access or manipulate prompts.
Escalating privileges by exploiting weak permission controls within the LLM environment.

Prevention:

Enforce robust access control mechanisms between LLM plugins.
Implement fine-grained permission models to restrict plugin interactions and enforce least privilege.

7. Data Leakage

Vulnerability:

Data leakage in LLMs exposes sensitive information, leading to privacy and security breaches.

Common Vulnerabilities Examples:

Mishandling of user data within the LLM ecosystem, leading to unauthorized access.
Insufficient data sanitization, allowing sensitive information to be exposed in outputs.

Prevention:

Implement strong data encryption and access controls to protect sensitive information.
Regularly audit data-handling processes and enforce strict data sanitization practices.

8. Excessive Agency

Vulnerability:

When LLMs interface with other systems without proper restrictions, it can lead to undesirable operations and actions.

Common Vulnerabilities Examples:

LLMs performing uncontrolled actions or accessing unauthorized resources through APIs.
Lack of boundary checks allowing unrestricted interaction with external systems.

Prevention:

Implement strict controls and boundary checks in APIs to restrict LLM actions.
Apply rate limiting and access controls to prevent excessive usage of external resources.

9. Overreliance

Vulnerability:

Overreliance on LLMs can result in misinformation or inappropriate content generation, leading to legal issues and reputational damage.

Common Vulnerabilities Examples:

Blindly trusting LLM outputs without human verification or oversight.
Relying solely on LLMs for critical decision-making without considering potential biases or errors.

Prevention:

Establish human oversight and validation processes to verify LLM outputs.
Use LLMs as decision-support tools rather than sole authorities in critical tasks.

10. Insecure Plugins

Vulnerability:

Plugins connecting LLMs to external resources may be exploited if they accept free-form text inputs, leading to malicious requests and undesired behaviors.

Common Vulnerabilities Examples:

Allowing unfiltered text inputs in plugins, enabling injection attacks or remote code execution.
Lack of input validation in plugins, leading to unauthorized access or data manipulation.

Prevention:

Enforce strict input validation and sanitization in plugin interfaces.
Perform security audits and code reviews for plugins to ensure robustness against potential attacks.

Securing Language Model Systems against these OWASP Top 10 vulnerabilities demands a comprehensive approach involving stringent validation, access controls, data monitoring, and constant vigilance. As the capabilities of LLMs continue to evolve, so must their security measures to mitigate emerging threats and safeguard against potential risks.

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