Just the other day, I was experimenting with dialogue-based LLM jailbreaking and managed to crack GPT-4 and GPT-4o multiple times, unleashing a chaotic mix of humorous responses. But the fun and games stop when your system gets hacked, data leaks, and you’re hit with unimaginable legal and financial consequences.

As LLMs evolve, especially with Agentic RAG systems that can access and manage data, we must ensure their security to prevent any damaging outcomes.

In this article, I’ll be teaching you about the pillars of LLM security, different risks and vulnerabilities involved, and the best practices to keep these models — and your systems — safe.

What is LLM Security?

LLM security involves identifying and mitigating vulnerabilities in large language models, such as their tendency to spread misinformation or generate harmful content. The range of potential vulnerabilities is vast, and companies prioritize them differently based on their unique needs.

For example, financial institutions may focus on preventing data leakage and minimizing excessive agency vulnerabilities, while chatbot companies might prioritize addressing bias and toxic behavior.

Failing to address these vulnerabilities can lead to catastrophic outcomes. For instance, the spread of false information due to insecure data and models can result in a loss of trust, legal consequences, and long-term damage to a company’s reputation.

4 Pillars of LLM Security

LLM security generally falls into four key areas: data security, model security, infrastructure security, and ethical considerations. Addressing these areas requires a blend of traditional cybersecurity techniques and protective measures specific to LLMs.

Data Security

LLMs require vast training datasets, which expose numerous data vulnerabilities. These include the potential to perpetuate bias, spread false information, or leak confidential data such as personally identifiable information (PII).

More advanced LLM applications, such as RAG and Agentic systems, can access and manipulate databases, which can be highly destructive if not carefully safeguarded. Thus, curating the training dataset and preventing data manipulation and poisoning are critical.

Model Security

Model security involves protecting the structure and functionality of an LLM from unauthorized changes. Such alterations can compromise the model’s effectiveness, reliability, and security, leading to biases, exploitation of vulnerabilities, or performance degradation.

Since LLMs can be targeted from multiple angles, it’s crucial to ensure that the model remains intact and operates as intended without being compromised.

Infrastructure Security

The environments hosting LLMs must be thoroughly secured against various threats. This includes implementing firewalls, intrusion detection systems, and robust physical security measures. Hardware protection, encryption protocols, and secure hosting environments are also essential to provide comprehensive defense against potential attacks.

Ethical Considerations

Ethical considerations in LLM security are crucial to preventing harm and ensuring responsible use. Key vulnerabilities include the generation of harmful content, such as misinformation, hate speech, and biased outputs that promote stereotypes and discrimination.

A little confused? Don’t worry — LLM security is a complex subject. As you dive deeper into the vulnerabilities and attacks affecting LLM systems, you’ll gradually gain a clearer understanding of how the four pillars of LLM security interrelate and come into play.

LLM Vulnerabilities

Many LLM vulnerabilities are not tied to a single cause. This means that failing to uphold any of the four pillars of security can lead to significant risks. For instance, neglecting ethical considerations and data security can result in biased responses, while a lack of model security and data security can lead to data leakage.

These vulnerabilities can be categorized into several areas, including potential harms, risks related to personally identifiable information (PII), threats to brand reputation, and technical weaknesses.

Harm Vulnerabilities

LLMs can inadvertently cause significant harm through various means:

• Misinformation & Disinformation: Spreading harmful lies or propaganda.
• Bias and Discrimination: Perpetuating stereotypes or unfair treatment of individuals based on race, gender, or other characteristics.
• Toxicity: Generating content that is harmful, offensive, or abusive.

This category also includes risks like promoting hate speech, encouraging self-harm, and generating inappropriate content, and more.

PII (Personally Identifiable Information) Vulnerabilities

PII vulnerabilities expose users to risks related to the mishandling or leakage of personal data:

• API and Database Access: Unauthorized access to sensitive databases through APIs.
• Direct PII Disclosure: Directly revealing personally identifiable information.
• Session PII Leak: Unintentional exposure of PII through session management flaws.
• Social Engineering PII Disclosure: PII obtained through manipulative social engineering tactics.

Brand Vulnerabilities

Brand vulnerabilities expose organizations to risks that can harm their reputation and legal standing:

• Contracts: Risks associated with the improper handling of legal documents or agreements.
• Excessive Agency: When an LLM acts beyond its intended scope, potentially leading to unintended consequences.
• Political Statements: Generating content that involves politically sensitive or controversial topics.
• Imitation: The risk of LLMs mimicking individuals or organizations without authorization.

Technical Vulnerabilities

Technical vulnerabilities involve risks associated with the underlying technology and infrastructure:

• Debug Access: Unauthorized access through debugging tools that can expose sensitive data.
• Role-Based Access Control (RBAC): Weaknesses in enforcing proper access controls.
• Shell Injection: Execution of unauthorized commands on the server hosting the LLM.
• SQL Injection: Exploiting vulnerabilities in databases that the LLM interacts with.
• DOS Attacks: Disrupting the availability of LLM services through Denial-of-Service attacks.

With so many types of vulnerabilities to consider, it can feel overwhelming to ensure comprehensive security for LLMs. But don’t worry — in the next sections, I’ll walk you through how to detect these vulnerabilities and protect your systems effectively.

LLM Vulnerability Detection

Detecting LLM vulnerabilities boils down to two main methods: using LLM benchmarks and red-teaming through simulated attacks.

Standardized LLM Benchmarks

Benchmarks are crucial for assessing risks across common vulnerability categories. (In fact, here is a great article on everything you need to know about LLM benchmarks).

For instance, TruthfulQA and FEVER evaluate an LLM’s ability to avoid misinformation, while tools like HaluEval specifically test for hallucination risks.

There are numerous published LLM benchmarks focusing on specific vulnerabilities. While these benchmarks offer a solid foundation, they might not cover every potential issue and could be outdated, especially for custom LLMs targeting specific vulnerabilities. This is where red-teaming becomes crucial.

LLM Red-Teaming through Simulated Attacks

Red-teaming involves simulating realistic attack scenarios to uncover vulnerabilities that benchmarks might overlook. It typically involves generating targeted prompts that exploit specific vulnerabilities and evolving these prompts into more effective adversarial attacks to provoke vulnerable responses. (You can learn more about red-teaming from this article I’ve written).

Some effective red-teaming methods include:

1. Direct Search (Human Red-teaming): This method involves systematically probing the LLM with various inputs to uncover vulnerabilities, manually crafting prompts to elicit unintended behaviors or unauthorized actions. It’s effective but not scalable due to the human effort required.

2. Token manipulation: By manipulating text tokens, like replacing words with synonyms, attackers can trigger incorrect model predictions. Techniques like TextFooler and BERT-Attack follow this approach, identifying and replacing key vulnerable words to alter model output.

3. Gradient-Based Attacks: These attacks use a model’s loss gradients to create inputs that cause failures. For example, AutoPrompt uses this strategy to find effective prompt templates, and Zou et al. explored adversarial triggers by appending suffix tokens to malicious prompts (however, gradient-based attacks only work on white-box LLMs that allow access to model parameters).

4. Algorithmic Jailbreaking: This involves techniques like Base64 encoding, character transformations (e.g., ROT13), or prompt-level obfuscations to bypass restrictions. These methods can cleverly disguise malicious inputs to trick the LLM.

5. Model-based Jailbreaking: This approach scales jailbreaking by automating the creation of adversarial attacks. It begins with simple synthetic red-teaming inputs that are iteratively evolved into more complex attacks, such as prompt injection, probing, and gray-box attacks.

To understand this synthetic data generation and data evolution better, I recommend reading the article on using LLMs for synthetic data generation, but in short, each evolution increases the complexity, allowing for the discovery of vulnerabilities that might be missed by less sophisticated methods. This strategy is highly effective because it can generate creative and targeted attacks without requiring continuous human input, making it ideal for large-scale vulnerability detection.

6. Dialogue-based Jailbreaking (Reinforcement Learning): Dialogue-based jailbreaking is the most effective jailbreaking technique an requires two models: the target LLM and a red-teamer model trained through reinforcement learning to exploit the target’s vulnerabilities.

The red-teamer generates adversarial prompts, receiving feedback based on the harmfulness of the target LLM’s responses. This feedback loop enables the red-teamer to refine its tactics, leading to increasingly effective attacks. By continuously learning from the target’s outputs, this method can uncover deep, complex vulnerabilities, providing valuable insights into how they might be exploited and fixed. For example, Prompt Automatic Iterative Refinement (PAIR), a dialouge-based Jailbreaking technique, has been found to require fewer than twenty queries to produce a jailbreak, which is much more efficient than existing algorithms.

However, implementing your own red-teamer is error-prone and time consuming. Fortunately, you no longer have to implement everything from scratch, and can use DeepEval ⭐ instead. DeepEval is the open-source evaluation framework for LLMs, and enables anyone to easily red-team and detect LLM vulnerabilities a few lines of code:

pip install deepeval

from deepeval import RedTeamer

target_purpose = "Provide financial advice, investment suggestions, and answer user queries related to personal finance and market trends."
target_system_prompt = "You are a financial assistant designed to help users with financial planning, investment advice, and market analysis. Ensure accuracy, professionalism, and clarity in all responses."

red_teamer = RedTeamer(
    target_purpose=target_purpose
    target_system_prompt=target_system_prompt,
)

DeepEval's RedTeamer scans for over 40 vulnerabilities and uses more than 10 adversarial techniques, including reinforcement learning based Jailbreaking.

OWASP Top 10 LLM Security Risks

The OWASP Top 10 LLM Security Risks, crafted by 500 experts and 126 contributors from various fields, outlines critical risks in LLMs. These include both vulnerabilities and attacks that we’ve previously discussed.

Here’s a quick look at the top 10 risks:

1. Prompt Injection: Attackers manipulate LLMs using crafted prompts to execute unauthorized actions. Prevention includes access restrictions, role-based permissions, and strict content filtering.
2. Insecure Output Handling: Risks arise from improperly handled LLM outputs, like unvalidated SQL queries. Implement zero-trust principles and secure output handling standards to mitigate.
3. Training Data Poisoning: Maliciously altered training data leads to biased LLM outputs. Ensure data source reliability, verify data authenticity, and validate outputs.
4. Model Denial of Service (DoS): Overloading the LLM with excessive inputs or complex queries impairs functionality. Monitor resource use, limit inputs, and cap API requests.
5. Supply Chain Vulnerabilities: Compromised data or outdated components affect model predictions. Conduct thorough supplier assessments, monitor models for abnormal behavior, and apply regular patches.
6. Sensitive Information Disclosure: Exposure of sensitive data during LLM interactions. Implement data sanitization and train staff on data handling best practices.
7. Insecure Plugin Design: Plugins with security gaps can lead to unauthorized actions. Use least privilege access, verify plugins for security, and employ authentication protocols.
8. Excessive Agency: Overly permissive LLMs perform unintended actions. Validate functionalities and maintain oversight with human-in-the-loop practices.
9. Overreliance: Excessive dependency on LLMs without proper controls leads to errors. Monitor LLM outcomes regularly and establish robust verification mechanisms.
10. Model Theft: Unauthorized access to LLMs allows data extraction or functionality replication. Strengthen access controls, monitor usage logs, and prevent side-channel attacks.

LLM Security Best Practices for Vulnerability Mitigation

Once LLM vulnerabilities are detected, pinpointing the exact cause can be challenging due to their multifactorial nature, as previously discussed. To address this, it’s crucial to monitor the OWASP Top 10 vulnerabilities, consider the four pillars of LLM security, and adhere to the best practices, which I’ll outline below.

1. Enhancing Model Resilience: To build robust LLMs, focus on adversarial training and incorporating differential privacy mechanisms. Adversarial training involves exposing the model to adversarial examples during its development, which enhances its ability to counteract attacks. Differential privacy introduces randomness into data or model outputs to prevent identification of individual data points, safeguarding user privacy while enabling broad learning from aggregated data.
2. Implementing Robust Controls: Strengthen security with comprehensive input validation mechanisms and strict access controls. Input validation ensures that only legitimate data is processed, guarding against malicious inputs and prompt injections. Access controls limit interactions with the LLM to authorized users and applications, using authentication, authorization, and auditing to prevent unauthorized access and breaches.
3. Securing Execution Environments: Create secure execution environments to isolate LLMs from potential threats. Techniques such as containerization and trusted execution environments (TEEs) protect the model’s runtime and operational integrity. Adopting federated learning also enhances security by allowing models to train across multiple servers without centralizing sensitive data.
4. Human-in-the-Loop and Tracing: Integrate human oversight into your LLM processes to catch errors or malicious activities that automated systems might miss. Implement tracing to track the flow of data and decisions within the LLM for greater transparency and accountability.
5. Monitoring in Production: Finally, effective monitoring is crucial for maintaining LLM security in production. Regularly review access controls, data usage, and system logs to detect and address anomalies or unauthorized activities. Implementing an incident response plan is vital for promptly managing security breaches and disruptions.

Confident AI offers vulnerability production monitoring for various use cases, including LLM chatbots and Text-to-SQL. It provides real-time safety guardrails to detect security issues, automated for any custom use case and metric. Additionally, Confident AI supports including human-in-the-loop processes and tracing, ensuring comprehensive security and oversight.

Conclusion

Today, we’ve delved into the fundamentals of LLM security, exploring critical areas such as data, model, infrastructure, and ethical considerations. We discussed various vulnerabilities, including misinformation, bias, and technical risks like SQL injection and PII leaks.

We also examined the importance of detecting these vulnerabilities through benchmarks and red-teaming methods, including advanced techniques like dialogue-based jailbreaking and synthetic data generation, and how DeepEval simplifies this process into a few lines of code. Moreover, we highlighted how effective monitoring in production, using platform like Confident AI can provide real-time vulnerability evaluations to safeguard your systems against emerging threats. This comprehensive approach ensures not only the robustness of your models but also their safe and ethical deployment.

If you found this article useful, give DeepEval a star on GitHub ⭐ to stay updated on the latest best open-source approach to tackling LLM security as we continue to support more security features.