Chevrolet of Watsonville, a car dealership, introduced a ChatGPT-based chatbot on their website. However, the chatbot falsely advertised a car for $1, potentially leading to legal consequences and resulting in a substantial bill for Chevrolet. Incidents like these highlight the importance of implementing security measures to LLM applications. 1
Explore top LLM security tools that can protect your large language model applications:
Before comparing LLM security tools, we analyzed them under three categories:
As we concentrate on LLM security tools, we excluded LLMOps tools and other large language models (LLMs) that cannot identify critical vulnerabilities or any security breach. We also did not mention tools that provide AI governance services that check for ethical behavior and data privacy regulations.
The table shows LLM security solutions listed on their category and number of employees of the vendors.
AI governance tools evaluate AI models for efficacy, bias, robustness, privacy, and explainability, providing actionable strategies for risk mitigation and standardised reporting. AI governance tools can help with LLM security assessments, ensuring that LLMs are secure, trustworthy, and compliant with relevant regulations, thereby enhancing the overall safety and reliability. Some of these tools include:
Credo AI is an AI governance platform that helps companies adopt, scale, and govern AI. Credo AI offers GenAI Guardrails, which provide governance features to support the safe adoption of generative AI technologies. Some of the features are:
Fairly AI, acquired by Asenion, is a focused AI governance, risk management and compliance tool to help organizations manage AI projects securely and effectively from the start. Fairly AI can be useful to detect and react on LLM security risks by features like:
Fiddler is an enterprise AI visibility tool that enhances AI observability, security, and governance. Fiddler helps organizations ensure LLMs are secure, compliant, and high-performing throughout their lifecycle. Its key products and capabilities include:
Holistic AI is an AI governance tool that helps ensure compliance, mitigate risks, and enhance the security of AI systems, including large language models (LLMs). It provides system assessments for efficacy, bias, privacy, and explainability, and continuous monitoring of global AI regulations. Some of its relevant features include:
Nexos.ai is an enterprise-grade LLM orchestration and gateway platform that enables organizations to integrate, manage, and monitor multiple AI models through a unified interface. It also provides AI governance and LLM security capabilities, including:
AI security tools provide security measures for artificial intelligence applications by employing advanced algorithms and threat detection mechanisms. Some of these tools can be deployed for LLMs to ensure the integrity of these models.
Synack is a cybersecurity company that focuses on providing crowdsourced security testing services. Synack platform introduces a set of capabilities to identify AI vulnerabilities and reduce other risks involved in LLM applications. Synack is suitable for various AI implementations, including chatbots, customer guidance, and internal tools. Some critical features it offers include:
WhyLabs LLM Security offers a comprehensive solution to ensure the safety and reliability of LLM deployments, particularly in production environments. It combines observability tools and safeguarding mechanisms, providing protection against various security threats and vulnerabilities, such as malicious prompts. Here are some of the key features WhyLabs’ platform offers:
CalypsoAI Moderator can secure LLM applications and ensure that organizational data remains within its ecosystem, as it neither processes nor stores the data. The tool is compatible with various platforms powered by LLM technology, including popular models like ChatGPT. Calypso AI Moderator features help with
Adversa AI specializes in cyber threats, privacy concerns, and safety incidents in AI systems. The focus is on understanding potential vulnerabilities that cybercriminals may exploit in AI applications based on the information about the client’s AI models and data. Adversa AI conducts:
GenAI-specific tools safeguards the integrity and reliability of language-based AI solutions. These tools can be cybersecurity tools that tailor their services for LLMs or platforms and toolkits specifically developed for securing language generation applications.
Praetorian is a cybersecurity company that specializes in providing advanced security solutions and services. Praetorian can enhance company security posture by offering a range of services, including vulnerability assessments, penetration testing, and security consulting. Praetorian employs adversarial attacks to challenge LLM models. Praetorian’s platform allows users to:
LLM Guard, developed by Laiyer AI, is a comprehensive and open-source toolkit crafted to enhance the security of Large Language Models (LLMs) through bug fixing, documentation improvement, or spreading awareness. The toolkit allows to
Lakera Guard is a developer-centric AI security tool crafted to safeguard Large Language Models (LLMs) applications within enterprises. The tool can integrate with existing applications and workflows through its API, remaining model-agnostic, enabling organizations to secure their LLM applications. Noteworthy features include:
Lasso Security’s LLM Guardian integrates assessment, threat modeling, and education to protect LLM applications. Some of the key features include:
Open-source coding platforms and libraries empower developers to implement and enhance security measures in AI and Generative AI applications. Some of them are specifically developed for LLM security, while some others can be deployed to any AI model.
The table shows open-source LLM security coding frameworks and libraries according to their Github rates.
Guardrails AI is an open-source library for AI applications security. The tool consists of two essential components:
Guardrails AI helps establishing and maintaining assurance standards in LLMs by
Garak is a thorough vulnerability scanner designed for Large Language Models (LLMs), aiming to identify security vulnerabilities in technologies, systems, applications, and services utilizing language models. Garak’s features are listed as:
Rebuff is a prompt injection detector designed to safeguard AI applications from prompt injection (PI) attacks, employing a multi-layered defense mechanism. Rebuff can enhance the security of Large Language Model (LLM) applications by
Explore more on Vector database and LLMs.
The G3PO script serves as a protocol droid for Ghidra, aiding in the analysis and annotation of decompiled code. This script functions as a security tool in reverse engineering and binary code analysis by utilizes large language models (LLMs) like GPT-3.5, GPT-4, or Claude v1.2. It providers users with
Vigil is a Python library and REST API specifically designed for assessing prompts and responses in Large Language Models (LLMs). Its primary role is to identify prompt injections, jailbreaks, and potential risks associated with LLM interactions. Vigil can deliver:
LLMFuzzer is an open-source fuzzing framework specifically crafted to identify vulnerabilities in Large Language Models (LLMs), focusing on their integration into applications through LLM APIs. This tool can be helpful for security enthusiasts, penetration testers, or cybersecurity researchers. Its key features include
EscalateGPT is an AI-powered Python tool that identifies privilege escalation opportunities within Amazon Web Services (AWS) Identity and Access Management (IAM) configurations. It analyzes IAM misconfigurations and provides potential mitigation strategies by using different OpenAI’s models. Some features include:
EscalateGPT’s performance may vary based on the model it utilizes.For instance, GPT4 demonstrated the ability to identify more complex privilege escalation scenarios compared to GPT3.5-turbo, particularly in real-world AWS environments.
BurpGPT is a Burp Suite extension designed to enhance web security testing by incorporating OpenAI’s Large Language Models (LLMs). It offers advanced vulnerability scanning and traffic-based analysis capabilities, making it suitable for both novice and experienced security testers. Some of its key features include:
While open-source libraries and frameworks offer valuable tools for protecting LLM applications, secure code generation also depends on using safer programming languages. A notable example is Microsoft’s rewrite of its core cryptographic libraries, SymCrypt, from C to Rust, a memory safety language.3
Though not LLM-generated, this effort demonstrates how choosing secure-by-design languages can eliminate entire classes of vulnerabilities. As LLMs take on more code-writing tasks, pairing them with safer languages like Rust can reduce the risk of generating insecure or exploitable code.
Agentic security refers to security of AI agents:
The Model Context Protocol (MCP) is the industry standard for connecting AI agents to tools. An MCP gateway acts as a firewall for these connections, preventing agents from being hijacked by the tools they use.
These tools focus on managing the credentials, “intent,” and privileges of these autonomous digital citizens.
Since agents act in non-deterministic ways, static security checks are insufficient. Autonomous red teaming approach constantly attacks agents to find weaknesses.
LLM security refers to the security measures and considerations applied to Large Language Models (LLMs), which are advanced natural language processing models, such as GPT-3. LLM security involves addressing potential security risks and challenges associated with these models, including issues like:
1. Data Security: Language models may generate inaccurate or biased content due to their training on vast datasets. Another data security issue is the data breaches where unauthorized users gain access to the sensitive information.
Solution: Use Reinforcement Learning from Human Feedback (RLHF) to align models with human values and minimize undesirable behaviors.
2. Model Security: Protect the model against tampering and ensure the integrity of its parameters and outputs.
Measures: Implement security to prevent unauthorized changes, maintaining trust in the model’s architecture. Use validation processes and checksums to verify output authenticity.
3. Infrastructure Security: Ensure the reliability of language models by securing the hosting systems.
Actions: Implement strict measures for server and network protection, including firewalls, intrusion detection systems, and encryption mechanisms, to guard against threats and unauthorized access.
4. Ethical Considerations: Prevent the generation of harmful or biased content and ensure responsible model deployment.
Approach: Integrate ethical considerations into security practices to balance model capabilities with the mitigation of risks. For this, applyAI governance toolsand methods.
LLM security concerns may lead to:
– Loss of Trust: Security incidents can erode trust, impacting user confidence and stakeholder relationships.
– Legal Repercussions: Breaches may lead to legal consequences, especially concerning regulated data derived from reverse engineering LLM models.
– Damage to Reputation: Entities using LLMs may face reputational harm, affecting their standing in the public and industry.
On the other hand, compromise security can ensure and improve:
– Reliabile and consistent LLM performance in various applications.
– Trustworthiness of LLM outputs, preventing unintended or malicious outcomes.
– Responsible LLM security assurance for users and stakeholders.
OWASP (Open Web Application Security Project) has expanded its focus to address the unique security challenges associated with LLMs. Here is the full list of these LLM security risks and tools to mitigate them:
1. Prompt Injection
Manipulating the input prompts given to a language model to produce unintended or biased outputs.
Tools & methods to use:
– Input validation: Implement strict input validation to filter and sanitize user prompts.
– Regular expression filters: Use regular expressions to detect and filter out potentially harmful or biased prompts.
2. Insecure Output Handling
Mishandling or inadequately managing the outputs generated by a language model, leading to potential security or ethical issues.
Tools & methods to use:
– Post-processing filters: Apply post-processing filters to review and refine generated outputs for inappropriate or biased content.
– Human-in-the-loop review: Include human reviewers to assess and filter model outputs for sensitive or inappropriate content.
3. Training Data Poisoning
Introducing malicious or biased data during the training process of a model to influence its behavior negatively.
Tools & methods to use:
– Data quality checks: Implement rigorous checks on training data to identify and remove malicious or biased samples.
– Data augmentation techniques: Use data augmentation methods to diversify training data and reduce the impact of poisoned samples.
4. Model Denial of Service
Exploiting vulnerabilities in a model to disrupt its normal functioning or availability.
Tools & methods to use:
– Rate limiting: Implement rate limiting to restrict the number of model queries from a single source within a specified time frame.
– Monitoring and alerting: Ensure continuous monitoring of model performance and set up alerts for unusual spikes in traffic.
5. Supply Chain Vulnerabilities:
Identifying weaknesses in the supply chain of AI systems, including the data used for training, to prevent potential security breaches.
Tools & methods to use:
– Data source validation: Verify the authenticity and quality of training data sources.
– Secure data storage: Ensure secure storage and handling of training data to prevent unauthorized access.
6. Sensitive Information Disclosure:
Unintentionally revealing confidential or sensitive information through the outputs of a language model.
Tools & methods to use:
– Redaction techniques: Develop methods for redacting or filtering sensitive information from model outputs.
– Privacy-preserving techniques: Explore privacy-preserving techniques like federated learning to train models without exposing raw data.
7. Insecure Plugin Design:
Designing plugins or additional components for a language model that have security vulnerabilities or can be exploited.
Tools & methods to use:
– Security audits: Conduct security audits of plugins and additional components to identify and address vulnerabilities.
– Plugin isolation: Implement isolation measures to contain the impact of security breaches within plugins.
8. Excessive Agency:
Allowing a language model to generate outputs with excessive influence or control, potentially leading to unintended consequences.
Tools & methods to use:
– Controlled generation: Set controls and constraints on the generative capabilities of the model to avoid outputs with excessive influence.
– Fine-tuning: Fine-tune models with controlled datasets to align them more closely with specific use cases.
9. Overreliance:
Excessive dependence on the outputs of a language model without proper validation or consideration of potential biases and errors.
Tools & methods to use:
– Diversity of models: Consider using multiple models or ensembles to reduce overreliance on a single model.
– Diverse training data: Train models on diverse datasets to mitigate bias and ensure robustness.
10. Model theft:
Unauthorized access or acquisition of a trained language model, which can be misused or exploited for various purposes.
Tools & methods to use:
– Model encryption: Implement encryption techniques to protect the model during storage and transit.
– Access controls: Enforce strict access controls to limit who can access and modify the model.
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