10 LLM Security Tools to Know in 2024

Who Is Responsible for LLM Security? 

The responsibility for LLM security primarily lies with organizations deploying these models. IT departments and cybersecurity teams must implement security measures and monitoring for potential threats. They ensure that best practices in data protection and AI ethics are followed. This includes setting strict access controls and regularly updating security protocols.

LLM security is a shared responsibility within an organization. Developers must build models with security in mind, integrating necessary safeguards from the onset. Users and stakeholders also share responsibility by remaining vigilant and reporting anomalies.

Key Features of LLM Security Tools 

Security solutions for large language models typically include the following capabilities.

Input Validation and Filtering

These tools check incoming data to check for anomalies, unauthorized commands, and malicious content. They act as a protective barrier, preventing injection attacks and the execution of harmful instructions that could compromise the model's functionality or security.

By implementing stringent input validation mechanisms, organizations can significantly reduce vulnerabilities associated with incorrect or manipulated data. These mechanisms decode and sanitize data inputs, preserving the integrity of the language model. Consistent filtering keeps the system free from disruptive elements that could lead to unexpected behaviors or failures.

Rate Limiting and Access Controls

Rate limiting and access controls counter excessive demand on LLMs and restrict unauthorized access. By setting a maximum number of requests per user, rate limiting prevents overload that could lead to denial-of-service attacks. It ensures resource availability and stability, preserving optimal system performance during high demand periods.

Access controls authenticate user identities and establish permissions, ensuring only authorized personnel can interact with critical functions of the LLM. These controls aid in prevention, by managing whom and what has entry to sensitive areas of the AI system.

Model Behavior Monitoring

Model behavior monitoring tracks the operations of an LLM to detect unusual patterns or unauthorized actions. Tools equipped with this feature use anomaly detection algorithms to flag deviations from normal behavior, indicating potential breaches or internal failures. Real-time alerts enable quick response to contain or mitigate threats before they cause significant harm.

Continuous monitoring enhances transparency by providing insights into model operations, supporting better understanding and management of security concerns. It allows administrators to fine-tune system parameters and improve defensive measures. 

Adversarial Input Detection

Adversarial input detection identifies and manages inputs crafted to deceive or destabilize language models. Such attacks can subtly alter model output, leading to incorrect or manipulated results. Detection tools analyze input patterns to uncover disguised threats, protecting the model from being compromised or manipulated through outsourced data.

By strengthening adversarial input defenses, organizations can preserve model accuracy and trustworthiness. This ensures that LLM outputs remain consistent and unaffected by malicious attempts to influence their operations. 

Bias Detection and Mitigation

Bias detection and mitigation focus on identifying and addressing inherent biases in LLMs that might skew outputs or decisions. LLM security tools analyze model outputs to ensure fairness, promoting neutrality and inclusivity. By recognizing biased patterns, organizations can apply corrective measures like re-training with more balanced data or adjusting algorithm parameters.

Mitigating bias preserves the integrity of decisions informed by LLMs and supports ethical AI development. By using these tools, companies can avoid inadvertently promoting discrimination and uphold standards of fairness in AI-driven processes. 

Tips from the expert:

In my experience, here are tips that can help you better secure large language models (LLMs):

• Leverage anomaly detection for fine-grained response analysis: Standard monitoring often focuses on input anomalies, but coupling it with anomaly detection in the output (response) can reveal hidden attack vectors like model manipulation or unauthorized data leakage through generated responses.
• Deploy API-based throttling to mitigate over-utilization risks: Rate limiting is crucial, but consider using API-based intelligent throttling based on behavioral patterns. This can detect unusual spikes in user behavior and automate context-aware throttling, reducing risks like distributed denial-of-service (DDoS) attacks.
• Implement model watermarking for output traceability: For critical deployments, consider embedding invisible watermarks in LLM outputs. This can track when, where, and how a model's responses are being used, helping detect misuse or illicit re-sharing of model data outputs.
• Use canary prompts to detect prompt manipulation: Canary tokens aren’t just for data exfiltration; consider using them as “bait” prompts within your LLM environment. These specialized prompts will trigger alerts if altered, identifying potential injection attempts or tampering.
• Regularly audit model logs for security insights: Maintain extensive logging of LLM interactions, including prompt histories and outputs, and set up automated audit processes to scan these logs for security incidents. Advanced correlation analysis between logs can help reveal patterns of subtle, long-term attacks that may otherwise go unnoticed.

Notable LLM Security Tools 

1. Pynt

‍

Pynt enhances API discovery by identifying LLM-based APIs that are increasingly integrated into applications today. Using dynamic analysis and traffic inspection, Pynt can detect APIs related to large language models (LLMs) and monitor their usage across your system. This capability ensures that any AI-related API endpoints, which often process sensitive or complex data, are fully mapped and included in the security testing scope.
Pynt also provides comprehensive support for identifying vulnerabilities in LLM APIs, the growing attack surface in AI-powered systems. By dynamically analyzing API traffic, Pynt detects potential weaknesses such as prompt injection and insecure output handling, which are specific to LLM-based APIs. These vulnerabilities are critical in ensuring that AI systems do not expose sensitive data or fall victim to malicious manipulation. Learn more about common LLM risks like prompt injection and insecure output handling.

‍

2. WhyLabs

‍

WhyLabs is a security and observability platform to protect AI applications, including large language models, from potential security risks. It provides real-time monitoring, flagging, and mitigation of threats, ensuring that AI systems remain secure and perform optimally.

Key features of WhyLabs include:

• Real-time threat detection: Monitors AI models for security risks such as prompt injections, jailbreak attempts, and data leaks, blocking harmful interactions before they impact the system.
• Performance and model drift monitoring: Continuously evaluates model health, identifying performance degradations and model drift to prevent issues from escalating.
• Bias detection and mitigation: Identifies biases in AI models and flags cohorts contributing to skewed outputs, ensuring fairness and compliance.
• Customizable security guardrails: Allows users to configure security measures based on their needs, such as using custom models, red teaming scenarios, and response validation techniques.
• Seamless integration: Supports over 50 integrations, making it compatible with various cloud providers and enabling observability for both proprietary and self-hosted AI models.

‍

3. LLM Guard

‍

LLM Guard by Protect AI is a security toolkit for protecting interactions with large language models. It focuses on preventing data leakage, filtering harmful content, and defending against prompt injection attacks.

License: MIT

Repo: https://github.com/protectai/llm-guard

GitHub stars: 1,000+

Contributors: 10+

Key features of LLM Guard include:

• Prompt injection prevention: Detects and neutralizes prompt injection attacks, ensuring that unauthorized commands do not compromise the LLM's behavior.
• Harmful language detection: Scans and sanitizes prompts and outputs to prevent the use of offensive or toxic language, preserving a safe environment for AI interactions.
• Data leakage prevention: Protects sensitive information by anonymizing inputs and outputs, preventing accidental or malicious exposure of personal or proprietary data.
• Bias detection and correction: Evaluates LLM outputs for biased language, ensuring fair and accurate responses by automatically flagging and mitigating biased content.
• Customizable scanning modules: Users can integrate various input and output scanners, such as toxicity filters, token limits, and relevance checks.

‍

4. Lasso Security

‍

Lasso Security is an end-to-end solution to address the security challenges posed by large language models. With a focus on protecting organizations from external cyber threats and internal vulnerabilities, it ensures safe and productive usage of LLM technology.

Key features of Lasso Security include:

• Shadow AI discovery: Automatically identifies the LLM tools and models being used within your organization, providing visibility into who is using them, where, and how.
• Threat detection: Monitors LLM interactions in real time, detecting potential security threats such as data leakage, malicious prompts, or unauthorized access, offering proactive defenses against evolving cyber threats.
• End-to-end protection: Goes beyond traditional security methods by covering both external threats and internal errors, securing the entire lifecycle of LLM deployments from data input to model output.
• User-friendly implementation: Designed for easy installation without requiring specialized AI or cybersecurity expertise. Organizations can get started quickly and ensure secure LLM operations with minimal setup.
• Real-time response: Provides rapid response capabilities, automatically blocking or flagging malicious interactions

‍

5. BurpGPT

BurpGPT is an extension for Burp Suite that integrates large language models to enhance the precision of web application security testing. With vulnerability scanning and traffic analysis capabilities, it allows security professionals to detect and mitigate threats more effectively.

License: Apache 2.0

Repo: https://github.com/aress31/burpgpt

Github stars: 2,000+

Key features of BurpGPT include:

• AI-enhanced vulnerability scanning: Uses LLMs to perform web vulnerability assessments, helping security teams identify weaknesses such as cryptographic flaws, misconfigurations, and zero-day exploits.
• Web traffic analysis: Uses AI to analyze web traffic, identifying potential security risks from incoming and outgoing data streams.
• AI co-pilot for testing: Acts as an AI assistant that improves the user’s testing capabilities, providing insights and automating complex security tasks.
• Custom-trained LLMs: The Pro edition supports the use of local, custom-trained models, allowing organizations to harness their internal security knowledge without sharing data with third-party services, ensuring client confidentiality and compliance.
• Guided integration: Integrates into existing Burp Suite workflows with accompanying documentation.

6. LLMFuzzer

‍

LLMFuzzer is an open-source fuzzing framework for testing large language models and their integrations via LLM APIs. It enables security researchers, penetration testers, and cybersecurity professionals to identify and exploit vulnerabilities in AI systems by automating the testing process.

License: Apache 2.0MIT

Repo: https://github.com/mnns/LLMFuzzer

Github stars: 200+

Key features of LLMFuzzer include:

• Fuzzing for LLMs: Provides fuzzing capabilities tailored to LLMs, helping users discover weaknesses in model responses and system interactions.
• LLM API integration testing: Tests the integration of LLMs in applications via APIs, ensuring secure and stable communication between systems and identifying potential security gaps.
• Range of fuzzing strategies: Uses various fuzzing techniques to simulate attacks, detect anomalies, and assess the resilience of LLM-powered systems against unexpected or malicious inputs.
• Modular and extendable architecture: Built with a modular design, allowing users to add custom fuzzing strategies, connectors, or testing modules.
• Roadmap for expanded features: Includes upcoming features like HTML report generation, multiple API connectors (e.g., JSON-POST, QUERY-GET), side-by-side LLM observation, and autonomous attack modes.

7. Vigil

‍

Vigil is a Python library and REST API to detect and mitigate security threats in LLM prompts and responses. By using a variety of scanners, Vigil can identify issues such as prompt injections, jailbreak attempts, and other vulnerabilities in LLM applications.

License: Apache 2.0

Repo: https://github.com/deadbits/vigil-llm

GitHub stars: 200+

Key features of Vigil include:

• Prompt injection detection: Scans LLM prompts for known injection techniques, defending systems from attempts to manipulate or exploit LLMs.
• Modular and extensible scanners: Uses multiple detection methods such as vector databases, YARA heuristics, and transformer models to identify risks. The modular architecture allows users to add custom scanners based.
• Canary tokens: Incorporates canary tokens into prompts to detect prompt leakage or hijacking, ensuring that malicious attempts to alter prompt behavior are swiftly identified and mitigated.
• REST API and Python library: Can be used as a REST API for integration into existing workflows or directly in Python applications for prompt and response analysis.
• Customizable and self-hosted: Supports custom detection signatures and can be self-hosted, giving organizations control over their security processes.

8. Rebuff

‍

Rebuff is a multi-layered prompt injection detection tool to protect AI applications from prompt injection attacks. By using a combination of heuristics, LLM-based detection, and canary tokens, Rebuff helps identify and mitigate potential vulnerabilities before they can be exploited.

License: Apache 2.0

Repo: https://github.com/protectai/rebuff

GitHub stars: 1,000+

Contributors: <10

Key features of Rebuff include:

• Heuristic-based filtering: Filters out malicious inputs before they reach the LLM, preventing common injection attacks and adversarial prompts from affecting the system.
• LLM-based detection: Uses a dedicated language model to analyze incoming prompts, detecting potential attacks in real time.
• Vector database for attack recognition: Stores embeddings of previous attacks in a vector database, allowing Rebuff to recognize and prevent similar threats from recurring by cross-referencing new inputs.
• Canary tokens for leak detection: Adds canary tokens to prompts, which can be monitored for leak detection. If a token is leaked, Rebuff identifies the issue and updates its database to prevent future breaches.
• Easy integration with SDKs: Offers Python and JavaScript/TypeScript SDKs, allowing users to easily integrate Rebuff into their AI systems.

9. Garak

Garak is a command-line tool for vulnerability scanning of LLMs. As a red-teaming and assessment kit, Garak identifies weaknesses in LLMs such as hallucinations, data leakage, prompt injections, and toxic outputs, helping ensure AI security.

License: Apache 2.0

Repo: https://github.com/leondz/garak

Github stars: 1,000+

Contributors: 20+

Key features of Garak include:

• Multi-faceted vulnerability scanning: Probes LLMs for various vulnerabilities, including prompt injections, hallucinations, misinformation, and jailbreaks.
• Diverse LLM support: Supports multiple LLM platforms, including Hugging Face models, OpenAI APIs, and Replicate models.
• Heuristic and LLM-based detection: Combines heuristic methods with LLM-based detection to identify and mitigate potential attacks or unwanted behaviors within the AI's outputs.
• Vector database for attack recognition: Stores embeddings of previous attacks in a vector database to recognize recurring vulnerabilities and prevent them from being exploited again.
• Flexible probing framework: Offers a range of probes, including encoding-based prompt injections, toxic content generation, and cross-site scripting (XSS).

10. CalypsoAI

‍

CalypsoAI is a security platform to protect enterprise-level AI applications, specifically those using LLMs. With security, observability, and integration capabilities, CalypsoAI provides protection for AI systems against both internal and external risks.

Key features of CalypsoAI include:

• 360-degree protection: Offers real-time scanning, flagging, and alerting to detect vulnerabilities and potential risks in LLM usage. Administrators can monitor insights into model behavior, decision-making, reliability, and performance.
• Customizable prompt moderation: Blocks flagged prompts based on configurable scanner settings, ensuring that only safe and compliant inputs are processed by the LLMs.
• Model-agnostic enablement: Supports custom-built and third-party LLMs, providing flexibility for organizations to experiment with and scale multimodal AI projects without being limited by LLM providers.
• API integration: CalypsoAI’s API enables rapid integration with existing workflows, boosting organizational productivity and ensuring smooth LLM deployment across the enterprise. 
• Compliance and regulatory alignment: Ensures that AI deployments remain compliant with evolving regulatory standards.

‍

Conclusion

LLM security tools play a crucial role in protecting AI systems from a range of vulnerabilities, ensuring the safe and ethical use of large language models. By incorporating advanced features like input validation, access controls, adversarial input detection, and bias mitigation, these tools help organizations protect their data, prevent misuse, and maintain trust in AI technologies. 

‍