As a security enthusiast exploring the rapidly evolving landscape of Large Language Models (LLMs), I’ve been fascinated by the unique security challenges they present. The OWASP Top 10 for LLMs has emerged as a crucial framework for understanding these vulnerabilities. As someone who is working in detection side of security for a long time, I was curios to find out the available resources to detect known vulnerabilities in LLM models. This which would help anyone building and using LLM in their business or organisation to secure their data and privacy.

Note: There will be overlap in functionalities in the recommended tools and that is totally obvious as developers tend to build solution to address multiple security issues than one.

1. Prompt Injection

Vulnerability Overview:
When attackers manipulate LLM behavior through carefully crafted inputs that override intended restrictions or controls.

Open Source Detection Tools:

• LLM-Guard: Provides sanitization and detection of malicious prompts
• Rebuff: Automated prompt injection detection

2. Improper Output Handling

Vulnerability Overview:
When LLM outputs are processed without proper validation, potentially leading to downstream vulnerabilities.

Open Source Detection Tools:

• LangKit: Output validation and sanitization
• SafeType: Type-safe output handling

Best Practices:

• Implement output validation
• Sanitize generated code
• Use content security policies

3. Training Data Poisoning

Vulnerability Overview:
Manipulation of training data to introduce vulnerabilities or biases into the model.

Open Source Detection Tools:

• IBM Adversarial Robustness Toolbox (ART): Provides data poisoning detection methods.
• SecML: Helps to wrap models from poisoning adversarial machine learning attacks
• Microsoft Presidio – Identifies sensitive data in training datasets to prevent unintentional bias or poisoning from leaked PII.

4. Unbounded Consumption

Vulnerability Overview:
Overloading LLM systems through resource-intensive requests.

Open Source Detection Tools:

• Coraza: Resource usage monitoring
• Fail2Ban: Detects repeated abusive requests and bans IPs automatically.

5. Supply Chain Vulnerabilities

Vulnerability Overview:
Risks associated with third-party models and dependencies.

Open Source Detection Tools:

• Model Scan: Scans machine learning models to detect unsafe code, supporting multiple model formats such as H5, Pickle, and Saved Model.
• GUAC: (Graph for Understanding Artifact Composition) – Aggregates software security metadata into a high-fidelity graph, providing a comprehensive view of the software supply chain.
• Agentic Radar – Includes mapping of detected vulnerabilities to well-known security frameworks OWASP Top 10 LLM Applications and OWASP Agentic AI – Threats and Mitigations

6. Sensitive Information Disclosure

Vulnerability Overview:
Unintended exposure of confidential information through model responses.

Open Source Detection Tools:

• Microsoft Presidio – Detect and anonymize Personally Identifiable Information (PII) in text and images. It utilizes Named Entity Recognition (NER) to identify sensitive entities like names, social security numbers, and medical identifiers, helping prevent inadvertent data leakage.
• gitleaks: Sensitive data detection
• HiddenGuard – A framework for fine-grained, safe generation in LLMs. It employs a specialized representation router to enable real-time, token-level detection and redaction of harmful or sensitive content, allowing models to generate informative responses while safeguarding confidential information.

7. Vector and Embedding Weaknesses

Vulnerability Overview:
Weaknesses in how vectors and embedding are generated, stored, or retrieved can be exploited by malicious actions (intentional or unintentional) to inject harmful content, manipulate model outputs, or access sensitive information.

Open Source Detection Tools:

• garak – LLM Vulnerability scanner: Command-line vulnerability scanner designed for LLMs. It employs static, dynamic, and adaptive probes to identify weaknesses such as hallucinations, data leakage, prompt injections, and toxic outputs.
• Microsoft Presidio : API security testing

8. Excessive Agency

Vulnerability Overview:
LLM taking unauthorized actions or making decisions beyond its scope.

Open Source Detection Tools:

• DeepEval: LLM testing framework that offers over 50 vulnerability types and more than 10 attack enhancement strategies for scanning LLM applications.

9. Misinformation

Vulnerability Overview:
Excessive trust in LLM outputs without proper verification.

Open Source Detection Tools:

• ChainForge – An open-source visual programming environment for prompt engineering and hypothesis testing of text generation LLMs.

10. System Prompt Leakage

Vulnerability Overview:
Refers to the risk that the system prompts or instructions used to steer the behavior of the model can also contain sensitive information that was not intended to be discovered.

Open Source Detection Tools:

• Model Scan – Scans machine learning models to detect unsafe code and vulnerabilities. It supports multiple model formats, including H5, Pickle, and SavedModel, commonly used in frameworks like PyTorch, TensorFlow, Keras, Scikit-learn, and XGBoost. By identifying potential security risks within models, ModelScan helps in preventing unauthorized access and exploitation.
• InjecGuard – Prompt guard model designed to detect and mitigate prompt injection attacks, which can lead to system prompt leakage.
• LLMFuzzer – Open-source fuzzing framework for testing LLMs and their integrations via LLM APIs. It automates the testing process to identify vulnerabilities, including prompt leakage, by generating diverse and unexpected inputs to evaluate the model’s responses.

Additional Resources

Documentation & Guidelines

• OWASP LLM Security Project
• MITRE ATLAS – Maps TTPs for Al-enabled systems
• Awesome LLM Security – Another awesome curation focusing on LLM security tools.

Monitoring Tools

• Langfuse: Open-source LLM monitoring. Helps to develop, monitor, evaluate, and debug AI applications.
• OpenAI Moderation API

Security Frameworks

• LLMSec: Comprehensive security framework
• Microsoft LLM Security Toolkit

Practical Implementation Tips for LLM Security

Implementing security measures for Large Language Models requires a layered approach that goes beyond simple input validation. Through my research, I’ve found several practical strategies that can significantly enhance your LLM application’s security posture.

Building a Robust Defense System

The foundation of LLM security starts with implementing multiple layers of protection in other words referred as defense in depth. Think of it as building a fortress – you don’t rely on just walls; you need guards, gates, and watchtowers. In the context of LLMs, this means combining input validation, output sanitization, and rate limiting. You can achieve this by creating a comprehensive wrapper around LLM interactions:

Regular Security Audits: Beyond the Basics

Security isn’t a set-and-forget feature – it requires constant vigilance and regular check-ups. Think of it like maintaining a high-performance vehicle. Regular security audits should become a cornerstone of your maintenance routine. This means implementing automated scanning tools that run regularly, not just when you remember to trigger them.

Consider setting up weekly automated scans that check for common vulnerabilities and misconfigurations. These scans should cover not just your LLM implementation, but also its surrounding infrastructure. Pay special attention to API endpoints, authentication mechanisms, and data storage solutions.

Creating an Effective Incident Response Plan

Even with the best defenses, security incidents can occur. The key difference between a minor hiccup and a major crisis often lies in how quickly and effectively you respond. Your incident response plan should be comprehensive yet practical. Start by documenting clear procedures for different types of incidents – from prompt injection attempts to data leaks.

Make sure to include:

• Clear escalation paths (who to contact and when)
• Detailed response procedures for different types of incidents
• Regular table top drills to ensure team familiarity with procedures
• Post-incident analysis templates to learn from each event

Monitoring and Alerting: Your Early Warning System

Effective monitoring is your radar system for detecting potential security threats. Set up comprehensive monitoring that covers:

• Unusual patterns in API usage
• Unexpected spikes in resource consumption
• Anomalies in response patterns
• Failed authentication attempts
• Suspicious input patterns

Configure alerts that notify the right people at the right time. But be careful – alert fatigue is real. Make sure your alerting thresholds are properly calibrated to avoid overwhelming your team with false positives.

Documentation

Maintain detailed documentation of your security implementations, but keep it practical and accessible. Your documentation should include:

• Security configurations and their rationale
• Response procedures and contact information
• Regular update logs
• Known issues and their workarounds
• Best practices specific to your implementation

Remember to keep this documentation updated – outdated security documentation can be worse than no documentation at all.

Future-Proofing Your Security Measures

The field of LLM security is evolving rapidly. What’s secure today might not be tomorrow. Build flexibility into your security infrastructure so you can adapt to new threats and implement new protection measures as they become necessary. Keep an eye on:

• New vulnerability discoveries
• Updated security best practices
• Emerging security tools and frameworks
• Changes in regulatory requirements

Remember, security is a journey, not a destination. So always stay curious, keep learning, and always be ready to adapt your security measures as new challenges emerge.