Learn how to protect against the most common LLM vulnerabilities
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
In-context learning
As users increasingly rely on Large Language Models (LLMs) to accomplish their daily tasks, their concerns about the potential leakage of private data by these models have surged.
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English to French Translation:
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A: At the beginning there was 10 cookies, then 2 of them were eaten, so 8 cookies were left. Then 5 cookieswere given toa friend, so 3 cookies were left. 3 cookies + 2 boxes of 2 cookies (4 cookies) = 7 cookies. Youhave 7 cookies.
English to French Translation:
Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?
Large Language Models (LLMs) such as OpenAI's GPT-3 and GPT-4 have revolutionized the way we interact with technology, from automated customer service to content creation.
Yet, their widespread adoption surfaces complex cybersecurity challenges that cannot be overlooked.
To maintain the integrity and reliability of systems that leverage LLMs, it's crucial to address risks such as unauthorized access and model exploitation.
In this article, we’ll be looking at 12 security tools currently in use to address vulnerabilities in LLMs, reflecting the ongoing commitment within the tech community to enhance the security measures surrounding these powerful AI models.
Here are the tools we cover:
- Lakera Guard
- WhyLabs LLM Security
- Lasso Security
- CalypsoAI Moderator
- BurpGPT
- Rebuff
- Garak
- LLMFuzzer
- LLM Guard
- Vigil
- G-3PO
- EscalateGPT
We’ll also be looking at some of the risks and the effectiveness of the tools against them.
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Lakera Guard
Lakera Guard is a developer-first Al security tool designed to protect Large Language Models (LLMs) applications across enterprises. It focuses on mitigating risks such as prompt injections, data loss, insecure output handling, and others. Lakera Guard's API seamlessly integrates with existing applications and workflows, it is completely model-agnostic, and enables developers to secure their LLM applications instantly.
Key features:
- Prompt Injection Protection: Lakera Guard offers practical defenses against direct and indirect prompt injection attacks that could lead to unintended downstream actions.
- Leakage of Sensitive Information: The tool helps mitigate risks when LLMs are connected to personally identifiable information (PII) or corporate data that should remain confidential.
- Detection of Hallucinations: It can identify outputs from models misaligned with the input context or expected behavior.
Lakera Guard is known for its ease of integration, requiring just a single line of code, and offers industry-leading response times, typically assessing prompts in less than 50ms. This makes it a user-friendly option for developers looking to secure their LLM applications without significant overhead or complexity.
Additionally, Lakera offers a solution called Lakera Red that focuses on AI red teaming. This solution is designed for effective stress testing of AI applications before deployment, providing an additional layer of security assurance.
Lakera Guard’s capabilities are continually evolving, backed by a proprietary vulnerability database that contains tens of millions of attack data points. This database grows daily, ensuring the tool's defenses are always up-to-date with the latest threat insights.
Try Lakera playground to see the tool in action.
**💡 Pro Tip: Check out the Prompt Engineering Guide for insights into prompt engineering techniques and best practices.**
WhyLabs LLM Security
WhyLabs LLM Security offers robust protection for LLMs against various security threats. It's designed to safeguard LLM applications against malicious prompts while ensuring safe response handling, which is crucial for maintaining the integrity of production LLMs.
Key features:
- Protection Against Data Leakage: It can detect targeted attacks aimed at leaking confidential data. This includes evaluating prompts for these attacks and blocking responses containing personally identifiable information (PII), which is essential for production LLMs.
- Prompt Injection Monitoring: WhyLabs monitors for malicious prompts designed to confuse the system into providing harmful outputs. This monitoring is vital to maintain a consistent and safe user experience.
- Misinformation Prevention: The platform helps identify and manage content generated by LLMs that might be misinformation or inappropriate due to "hallucinations." Preventing customer loss, legal issues, and reputational damage is crucial.
- OWASP Top 10 for LLM Applications: WhyLabs has implemented telemetry to capture the OWASP Top 10 for LLM Applications, which helps identify and mitigate vulnerabilities unique to LLMs. This feature allows teams to adopt best practices and keep their security measures current.
WhyLabs LLM Security offers a comprehensive solution for ensuring the safety and reliability of LLM deployments, particularly in production environments. It combines observability tools and safeguarding mechanisms to protect LLMs from various security threats and vulnerabilities.
**💡 Pro Tip: Understand the importance of ML Model Monitoring in maintaining the health and performance of AI systems.**
Lasso Security
Lasso Security presents an end-to-end solution explicitly designed for large language models (LLMs). It addresses the unique challenges and threats LLMs pose in a rapidly evolving cybersecurity landscape. Their flagship offering, LLM Guardian, is tailored to meet the specific security needs of LLM applications.
Key features:
- Security Assessments: Lasso Security conducts comprehensive evaluations of LLM applications to identify potential vulnerabilities and security risks. These assessments help organizations understand their security posture and the challenges they may face when deploying LLMs.
- Threat Modeling: The tool offers advanced threat modeling capabilities, enabling organizations to anticipate and prepare for potential cyber threats targeting their LLM applications. This proactive approach helps identify and mitigate risks before they materialize.
- Specialized Training Programs: Lasso Security provides specialized training programs to enhance teams' cybersecurity knowledge and skills working with LLMs. These programs equip personnel with the expertise to effectively manage and secure LLM technologies.
Lasso Security's LLM Guardian is a comprehensive solution that combines assessment, threat modeling, and education to offer robust protection for LLM applications. It ensures that organizations can safely harness the power of LLM technology while mitigating cybersecurity risks.
CalypsoAI Moderator
CalypsoAI Moderator is a comprehensive security solution for Large Language Models (LLMs). This tool addresses various security challenges associated with deploying LLMs in enterprises. Its key features cater to a wide range of security needs, making it a robust choice for organizations looking to safeguard their LLM applications.
Key features:
- Data Loss Prevention: This feature screens for sensitive data like code and intellectual property, ensuring that such information is blocked before leaving the organization. It is crucial to prevent the unauthorized sharing of proprietary information.
- Full Auditability: CalypsoAI Moderator provides a comprehensive record of all interactions, including prompt content, sender details, and timestamps. It enhances transparency and accountability in LLM usage.
- Malicious Code Detection: The solution can identify and block malware, thus safeguarding the organization's ecosystem from potential infiltrations via LLM responses.
- Easy-To-Use Interface: Designed to be user-friendly, CalypsoAI Moderator can be easily integrated into existing workflows, enhancing user experience without compromising security.
CalypsoAI Moderator is model agnostic, meaning it can be used with various platforms powered by LLMs, including popular models like ChatGPT. It can be deployed quickly, within 60 minutes, into a live environment, allowing organizations to secure their LLM applications promptly. It ensures that the organization's data does not leave its ecosystem, as CalypsoAI does not process or store it.
BurpGPT
BurpGPT is a Burp Suite extension designed to enhance web security testing by integrating OpenAI's Large Language Models (LLMs). It provides advanced vulnerability scanning and traffic-based analysis capabilities, making it a robust tool for beginners and seasoned security testers.
Key Features:
- Passive Scan Check: BurpGPT allows users to submit HTTP data to an OpenAI-controlled GPT model for analysis. This feature helps detect vulnerabilities and issues that traditional scanners might miss in scanned applications.
- Granular Control: Users have multiple OpenAI models to choose from and can control the number of GPT tokens used in the analysis.
- Integration with Burp Suite: BurpGPT integrates with Burp Suite, providing all native features for efficient analysis, including displaying analysis results within the Burp UI.
- Troubleshooting Functionality: It includes troubleshooting features via the native Burp Event Log, aiding users in resolving communication issues with the OpenAI API.
Application security experts develop the tool and continuously evolve it based on user feedback, ensuring it meets the dynamic needs of security testing. The Pro edition of BurpGPT supports local LLMs, including custom-trained models, offering greater data privacy and accuracy according to user needs.
Rebuff
Rebuff is a self-hardening prompt injection detector specifically designed to protect AI applications from prompt injection (PI) attacks. It employs a multi-layered defense mechanism to enhance the security of LLM applications.
Key Features:
- Multi-Layered Defense: Rebuff incorporates four layers of defense to provide comprehensive protection against PI attacks.
- LLM-Based Detection: Rebuff employs a dedicated LLM to analyze incoming prompts and identify potential attacks. This LLM-based approach allows for more nuanced and context-aware detection of threats.
- VectorDB: The tool stores embeddings of previous attacks in a vector database. This database is used to recognize and prevent similar attacks in the future, enhancing the tool's ability to adapt and respond to evolving threats.
- Canary Tokens: Rebuff adds canary tokens to prompts to detect leakages. The framework stores embeddings about the incoming prompt in the vector database, further strengthening its defense against future attacks.
Rebuff can detect prompt injections on user input and canary word leakage, making it versatile for different use cases. However, it is still in the prototype stage, meaning it is continuously evolving and cannot provide 100% protection against all prompt injection attacks.
Garak
Garak is an exhaustive LLM vulnerability scanner designed to find security holes in technologies, systems, apps, and services that use language models. It's a versatile tool simulating attacks and probing for vulnerabilities in various potential failure modes.
Key Features:
- Automated Scanning: Garak autonomously runs a range of probes over a model, managing tasks like finding appropriate detectors and handling rate limiting. It can perform a full standard scan and report without manual intervention.
- Connectivity with Various LLMs: The tool supports numerous LLMs, including OpenAI, Hugging Face, Cohere, and Replicate, as well as custom Python integrations. This broad compatibility makes it a flexible option for different LLM security needs.
- Self-Adapting Capability: The tool adapts itself over time. Each LLM failure found is logged and can be used to train Garak's auto red-team feature, which helps devise effective exploitation strategies for more thorough testing.
- Diverse Failure Mode Exploration: With a wide range of plugins, probes, and challenging prompts, Garak diligently explores different LLM failure modes. It reports each failing prompt and response, providing a comprehensive log for in-depth analysis.
Garak benefits security professionals and developers who must identify and understand the potential vulnerabilities in their LLM applications. By simulating various types of attacks and analyzing LLMs' responses, Garak helps preemptively identify and fix security issues.
LLMFuzzer
LLMFuzzer is an open-source fuzzing framework designed explicitly for Large Language Models (LLMs), mainly focusing on their integration into applications via LLM APIs. This tool is handy for security enthusiasts, pen-testers, or cybersecurity researchers keen on exploring and exploiting vulnerabilities in AI systems.
Key Features:
- Robust Fuzzing for LLMs: It is built to test LLMs for vulnerabilities rigorously.
- LLM API Integration Testing: It can test LLM integrations in various applications.
- Wide Range of Fuzzing Strategies: LLMFuzzer employs diverse strategies to identify vulnerabilities.
- Modular Architecture: Its design allows easy extension and customization according to specific testing needs.
LLMFuzzer is continuously evolving, with plans to add more attacks, HTML report outputs, support for multiple connectors, and an autonomous attack mode, among other features. For those interested in using LLMFuzzer, it can be cloned from its GitHub repository, and its modular design allows users to customize it according to their specific requirements.
LLM Guard
LLM Guard is a comprehensive tool designed to enhance the security of Large Language Models (LLMs). Developed by Laiyer.ai, it focuses on safeguarding interactions with LLMs, making it a critical tool for anyone using these models in their applications.
Key Features:
- Sanitization and Detection of Harmful Language: LLM Guard can identify and manage harmful language in LLM interactions, ensuring the content remains appropriate and safe.
- Prevention of Data Leakage: The tool is adept at preventing the leakage of sensitive information during LLM interactions, a crucial aspect of maintaining data privacy and security.
- Resistance Against Prompt Injection Attacks: LLM Guard offers robust protection against prompt injection attacks, ensuring the integrity of LLM interactions.
- Integration and Deployment: Designed for easy integration and deployment in production environments, LLM Guard can be seamlessly incorporated into existing systems.
**💡 Pro Tip: Learn about the intricacies of Retrieval-Augmented Generation in LLMs for enhanced model output.**
LLM Guard is an open-source solution, and it encourages community involvement, whether it's through bug fixing, feature proposing, documentation improvement, or spreading awareness about the tool.
Vigil
Vigil is a Python library and REST API designed explicitly for assessing Large Language Model (LLM) prompts and responses. Its primary function is to detect prompt injections, jailbreaks, and other potential risks associated with LLM interactions. The tool is currently in an alpha state and is considered experimental, mainly for research purposes.
Key Features:
- Prompt Analysis: Vigil is adept at analyzing LLM prompts for prompt injections and risky inputs, which is crucial for maintaining the integrity of LLM interactions.
- Modular Scanners: The tool has a modular design, making its scanners easily extensible. This design allows for the adaptation of Vigil to evolving security needs and threats.
- Diverse Detection Methods: Vigil employs various methods for prompt analysis, including vector database/text similarity, YARA/heuristics, transformer model analysis, prompt-response similarity, and Canary Tokens.
- Custom Detections: Users can create custom detections via YARA signatures, which adds to the tool's versatility.
Vigil's approach to securing LLMs, particularly against prompt injection attacks, is crucial given the growing use of these models in various applications. Its development and ongoing enhancement signify an essential step in strengthening the security posture of LLM-based systems.
G-3PO
A protocol droid for Ghidra for analyzing and annotating decompiled code.
The g3po.py script can be quite helpful as a security tool within the realm of reverse engineering and analysis of binary code. Leveraging a large language model (LLM) like GPT-3.5, GPT-4, or Claude v1.2 can provide several benefits:
- Automated Analysis. It can automatically generate comments and insights on decompiled code, which can help understand complex binary structures more quickly.
- Vulnerability Identification. The script might help identify potential security vulnerabilities in the code, given that LLMs can offer insights based on patterns and data they have been trained on.
- Code Annotation and Documentation. Suggesting meaningful names for functions and variables aids in making the code more readable and understandable, which is crucial in security analysis.
- Time Efficiency. It can save reverse engineers and security analysts time by automating part of the code review and documentation process.
However, it's important to remember that the accuracy and effectiveness of such a tool depend on the capabilities of the underlying LLM and the specific context of the code being analyzed.
EscalateGPT
EscalateGPT is an AI-powered Python tool identifying privilege escalation opportunities in Amazon Web Services (AWS) Identity and Access Management (IAM) configurations. This tool leverages the power of OpenAI's models to analyze IAM misconfigurations and suggest potential mitigation strategies.
Key features:
- IAM Policy Retrieval and Analysis: EscalateGPT can retrieve all IAM policies associated with users or groups. It then prompts the OpenAI API to identify possible privilege escalation opportunities and relevant mitigations.
- Detailed Results in JSON Format: The tool returns results in JSON format, detailing the path, Amazon Resource Name (ARN) of the policy with potential for exploitation, and recommended strategies to address the vulnerabilities.
- Performance with Different OpenAI Models: GPT4 identified more complex privilege escalation scenarios in testing scenarios than GPT3.5-turbo, particularly in real-world AWS environments.
The tool is designed to be user-friendly and integrates seamlessly into existing workflows. It benefits those involved in cloud security and AWS IAM configurations, helping to prevent common but often overlooked IAM misconfigurations.
Overview of Risks and Effectiveness of Tools
The tools mentioned above are designed to address various risks associated with Large Language Models (LLMs). These risks continually evolve, but several frameworks like OWASP and ATLAS/MITRE help systematize and categorize these risks.
Identifying the Main Risks Associated with LLMs
- Prompt Injection: This risk involves the unauthorized injection of malicious prompts into LLMs, leading to potential compromises in the system.
- Insecure Output Handling: If the outputs of LLMs are not validated, it can lead to security exploits, including unauthorized code execution.
- Training Data Poisoning: This involves tampering with the training data of LLMs, which can affect their responses and compromise security, accuracy, or ethical behavior.
- Model Denial of Service (MDoS): Overloading LLMs with resource-intensive operations can cause disruptions and increased operational costs.
- Supply Chain Vulnerabilities: Dependence on compromised components, services, or datasets can undermine system integrity, leading to data breaches and system failures.
- Data Leakage and Misinformation: The risk of exposing sensitive data and spreading misinformation due to LLM 'hallucinations' can lead to reputational damage and legal issues.
The OWASP Top 10 for Large Language Model Applications provides a comprehensive list of LLM applications' most critical vulnerabilities. This list highlights the potential impact, ease of exploitation, and prevalence of these vulnerabilities in real-world applications. It includes prompt injections, data leakage, inadequate sandboxing, and unauthorized code execution.
For more in-depth information on these risks and the OWASP Top 10 for LLMs, you can visit the OWASP website.
**💡 Pro Tip: Explore the challenges and strategies in AI Security to understand how to protect advanced AI models.**
Can the Tools Handle the Risks?
At Lakera, we approach LLM security comprehensively, aligning our solutions with the OWASP standards to mitigate risks associated with Large Language Models. Our multi-faceted approach encompasses several key areas:
- Prompt Injection and Output Handling: Lakera Guard is proficient in mitigating direct and indirect prompt injections. Direct prompt injections involve attackers manipulating the system prompt to exploit backend systems, while indirect injections occur when attackers control external sources that feed into the LLM. By addressing these concerns, Lakera Guard ensures secure and trustworthy LLM outputs backed by a vast threat intelligence database.
- Data Integrity and Supply Chain Security: Lakera Red focuses on pre-training data evaluation. This step is crucial in preventing training data poisoning, which can significantly alter LLM behavior. Additionally, Lakera Red scrutinizes LLM components to protect against vulnerabilities in the supply chain, thus ensuring the integrity of the model and its components.
- Comprehensive System Protection: Lakera's strategies extend to preventing Model Denial of Service (DoS) attacks and safeguarding sensitive information like Personally Identifiable Information (PII). Lakera Guard blocks suspicious users and manages API tokens to control who can access the LLM, thus preventing system overwhelm and unauthorized data exposure.
- Balancing AI Autonomy and Security: Lakera employs red teaming strategies for plugin assessment. This involves systematically evaluating a plugin's permissions and ensuring limited AI agency with continuous human oversight, addressing risks like overreliance and model theft.
- Continuous Improvement and Adaptation: Lakera Guard's capabilities constantly evolve, supported by a proprietary vulnerability database containing tens of millions of attack data points. This database grows daily, ensuring Lakera's defenses are always up-to-date with the latest threats.
This overview only scratches the surface of Lakera's comprehensive strategies for securing LLMs against many evolving threats. To get a better understand the depth and breadth of Lakera's approach and how it stands out in the realm of LLM security, also read how Lakera aligns with the ATLAS/MITRE framework.
**💡 Pro Tip: Delve into Large Language Model Evaluation to understand the metrics and methods for assessing LLM performance.**
Key Takeaways
Exploring the realm of cybersecurity for Large Language Models (LLMs), several specialized tools have emerged, each designed to fortify these AI systems against a plethora of risks.
These tools cater to a variety of security concerns, from data breaches and unauthorized prompt manipulations to the unintended generation of harmful content.
With the continuous evolution of threats in the LLM space, security solutions must be both flexible and forward-looking. The following 12 tools are part of the current landscape addressing the security of LLMs:
- Lakera Guard
- WhyLabs LLM Security
- Lasso Security
- CalypsoAI Moderator
- BurpGPT
- Rebuff
- Garak
- LLMFuzzer
- LLM Guard
- Vigil
- G-3PO
- EscalateGPT
Each of these tools has been crafted to navigate the complexities inherent in securing LLMs, demonstrating key features to manage existing and emerging threats. Tools like Lakera Guard, for instance, take a proactive stance, seeking out potential vulnerabilities before they manifest into larger-scale problems.
The integration of such security measures into the LLM deployment cycle is not just an added advantage but a necessity for ensuring a solid defense mechanism. As advancements in LLMs continue to accelerate, the corresponding security tools are called to progress in tandem, embracing more sophisticated technologies and methodologies.
Looking to the future, the trajectory of security tools for LLMs is likely to steer toward smarter, more autonomous, and fully integrated systems. This will aim to provide a vast, encompassing shield against the growing spectrum of potential cybersecurity threats.
Learn how to protect against the most common LLM vulnerabilities
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
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