Large Language Models (LLMs) are transforming industries by enabling advanced automation, intelligent decision-making, and more efficient information processing. However, as LLM adoption expands, so do the security risks associated with these powerful AI systems. Businesses must proactively safeguard their LLM applications against threats ranging from data leakage to adversarial attacks.
Building trust in AI starts with understanding the inherent risks of LLMs and implementing robust security measures. This guide explores the most common LLM security risks and best practices for mitigating them. We will also examine how the Fiddler AI Observability and Security platform ensure safer and more reliable LLM deployment.
Understanding Large Language Model (LLM) Security
What is LLM Security?
LLM security encompasses the practices and technologies that protect large language models and their infrastructure from unauthorized access, manipulation, and exploitation. Secure development practices ensure AI systems' reliability, transparency, and compliance while preventing potential threats.
Key aspects of LLM security include:
- Protecting model integrity against adversarial attacks.
- Securing sensitive data processed by LLMs.
- Monitoring and mitigating AI-specific risks such as hallucinations, bias, and prompt injection attacks.
- Implementing legal frameworks to maintain compliance and ethical AI use.
Why do LLMs Introduce New Security Challenges?
Unlike traditional software systems, LLMs rely on vast datasets and deep neural networks, which introduce unique vulnerabilities:
- Dynamic and Adaptive Responses: LLMs generate real-time outputs, making them susceptible to prompt injection attacks or manipulated outputs.
- Unstructured Data Complexity: LLMs operate on unstructured data like text, which is more nuanced than structured data. Their highly context-dependent accuracy requires advanced monitoring techniques beyond simple accuracy metrics to ensure quality, correctness, and safety.
- Data Privacy Risks: LLMs process sensitive data, increasing exposure to data leaks and regulatory compliance issues.
- Rapid Evolution: Frequent updates and fine-tuning create new attack surfaces.
Without proper observability, these risks can compromise the safety of LLM applications.
Common Cybersecurity Risks for LLM Applications
OWASP Top 10 LLM and GenAI Risks and Concerns
LLMs are exposed to various security threats that demand proactive defenses. Based on the Open Worldwide Application Security Project (OWASP) top 10 vulnerabilities, the following are some of the most significant risks organizations should address:
1. Prompt Injection Attacks
Threat actors manipulate inputs to override LLM safeguards, forcing the model to generate harmful or unintended responses. By injecting malicious prompts, they can bypass security measures, alter model behavior, and expose sensitive information such as confidential data, passwords, API keys, or security credentials. There are two main types of prompt injection attacks:
- Direct Prompt Injection: Attackers override existing instructions, bypass model alignment, or breach guardrails through direct interactions with the model.
- Indirect Prompt Injection: Attackers manipulate external sources — such as databases, documents, or websites — that the LLM relies on. They can influence or control the model’s outputs by poisoning these sources.
These attacks compromise business privacy, violate security guidelines, and pose significant data integrity and compliance risks.
2. Insecure Output Handling
Insecure Output Handling occurs when LLMs generate misleading, biased, or harmful content due to insufficient filters and oversight. Without proper valuation, businesses risk deploying inaccurate or potentially damaging AI-generated information.
3. Training Data and Model Poisoning
Adversaries manipulate data to fine-tune or alter model behavior, compromising its reliability. These attacks can introduce biases, distort decision-making, and undermine trust in the model’s outputs. Ensuring the integrity of training data is crucial to maintaining the accuracy and security of LLM applications.
4. Denial of Service (DoS) Attacks
Threat actors attempt to overwhelm LLMs by flooding them with excessive queries, requesting large responses, or exploiting vulnerabilities, ultimately causing slowdowns or system failures. These attacks disrupt business operations, leading to downtime and reduced service availability.
There are two types of DoS attacks:
- Model Denial of Service (DoS): Overloads an LLM by generating excessive queries or demanding significant responses, data drifts, or shutting the model down.
- Application Denial of Service (DoS): Targets applications or services using LLMs, making them unavailable to customers by flooding them with requests or exploiting software vulnerabilities to force a crash.
5. Supply Chain Vulnerabilities
LLMs rely on complex supply chains involving multiple third-party tools, APIs, and datasets, making them prone to various vulnerabilities. A weak link in this supply chain can compromise the integrity of training data, models, infrastructure, and deployment processes. These risks may affect open-source models and potentially lead to system-wide security breaches.
6. Sensitive Information Disclosure
LLMs process and store vast amounts of data, making them prime targets for unauthorized access and model theft. Sensitive information disclosure occurs when attackers exploit poorly secured models, using repeated queries to extract confidential data or proprietary model insights. Compliance violations, such as those under the General Data Protection Regulation (GDPR), can result in significant legal and financial penalties, further increasing security risks for businesses.
7. Misinformation
LLMs can generate flawed, biased, or misleading information due to incomplete or inaccurate embeddings. When trained on insufficient, biased, or low-quality raw data, their vector representations may lead to inaccuracies, producing unreliable or skewed outputs during inference. Data security throughout training is critical to maintaining model accuracy and preventing misinformation.
8. Vulnerable Plugins and API Security Risks
LLMs often interact with external systems through APIs, making them potential targets for cyber threats. Without proper security measures, attackers can exploit API vulnerabilities to manipulate data, inject malicious code, or gain unauthorized access.
One critical risk is remote code execution, where attackers leverage insecure API endpoints or plugins to execute arbitrary code on the system, potentially compromising the entire application. These vulnerabilities can lead to unintended outputs, security breaches, and loss of control over the AI system.
9. Excessive Agency and Automation Risks
Over-reliance on LLMs for decision-making without human oversight can result in unintended consequences, such as biased recommendations, misinformation, and security vulnerabilities. When AI agents operate with excessive autonomy, they may take actions that deviate from intended outcomes, increasing the risk of errors and reducing human control over critical processes.
Maintaining a balance between automation and human supervision is key to ensuring accuracy, security, and responsible AI use.
10. Unrestricted Usage
LLMs rely on extensive training with proprietary datasets and architectures. However, poorly optimized models can generate excessive queries, responses, or unintended outputs, straining system resources and driving up operational costs. This vulnerability may lead to hallucinations, system crashes, and inefficient resource utilization, ultimately affecting business efficiency.
Best Practices to Mitigate Generative AI Security Risks
Businesses must adopt proactive security strategies to ensure the secure deployment of LLMs. Best practices include:
1. Establish a Robust AI Governance Framework
A well-defined AI governance model is essential forsafeguarding LLM applications, enforcing security policies, maintaining compliance, and promoting responsible AI use. Organizations should:
- Define security policies for AI deployment to ensure consistency, risk mitigation, and secure operations.
- Implement accountability mechanisms to oversee AI decision-making, prevent misuse, and enhance oversight.
- Conduct regular audits to assess AI reliability, detect vulnerabilities, and ensure compliance with industry regulations.
2. Classify, Anonymize, and Encrypt Data Used with Generative AI
To protect sensitive information, organizations should:
- Identify and classify sensitive data before integrating it into AI models.
- Apply anonymization techniques to minimize the risk of data exposure.
- Encrypt data at rest and in transit to prevent unauthorized access and ensure compliance with security standards.
3. Implement Strong Access Controls and User Authentication
Strict access controls to LLMs are crucial for preventing unauthorized usage:
- Use role-based access control (RBAC) to limit user permissions.
- Implement multi-factor authentication (MFA) for model access.
- Regularly review and update access logs.
4. Conduct Regular Security Audits and Compliance Checks
Regular assessments are essential for identifying vulnerabilities early and maintaining AI security. Businesses should:
- Monitor LLM activity to detect anomalies and potential security threats.
- Conduct penetration testing to evaluate AI system defenses against cyberattacks.
- Ensure compliance with industry standards such as SOC 2, GDPR, and CCPA to meet regulatory requirements and protect sensitive data.
5. Train Employees on Generative AI Security Best Practices
Educating teams on AI security helps prevent unintentional breaches. Key training areas include:
- Recognizing and preventing prompt injection attacks.
- Securely handling AI-generated data.
- Following company AI governance guidelines.
6. Enforce Strict Policies on Handling Sensitive Work Data
Establishing clear policies on AI-generated data usage is essential for mitigating LLM security risks. Organizations should implement software security measures and input validation mechanisms to prevent unauthorized access and data manipulation. Key policies include:
- Prohibiting unauthorized data input into LLMs to minimize exposure to security threats.
- Restricting AI-generated outputs in critical decision-making processes to maintain accuracy and compliance.
- Logging all LLM interactions for traceability, ensuring accountability, and detecting potential security breaches.
7. Invest in AI-Specific Cybersecurity Tools and Monitoring Solutions
Traditional security tools do not provide AI-specific insights. Investing in LLM monitoring solutions ensures:
- Real-time detection of security threats
- Monitoring of hallucination, toxicity, PII leakage, and more
- Automated alerts for suspicious activities
8. Ensure API Security and Secure Third-Party Integrations
LLM-powered applications rely on APIs, making them a prime attack vector. To keep models safe, it is essential to:
- Conduct regular API security testing.
- Enforce authentication for API success.
- Monitor API usage for abnormal activity.
Strengthening AI Security with Fiddler
LLM security requires a proactive approach. At Fiddler, we help organizations build trust in AI with industry-leading security solutions. The Fiddler AI Observability and Security platform empowers businesses to:
- Detect Risks Early and Prevent AI Jailbreaking Attempts: The Fiddler platform integrates proprietary, fine-tuned Trust Models that proactively identify threats, including toxicity and jailbreak attempts. By monitoring attack patterns and vulnerabilities to mitigate potential exploits before they occur.
- Implement Guardrails: Industry’s fastest guardrails with <100ms response times, Fiddler Guardrails prevent harmful LLM outputs and ensure compliance with security policies.
- Accelerate Time to Market: The Fiddler platform operates at enterprise scale (processing 5+ million requests per day), enabling businesses to monitor LLM deployments in production while ensuring data remains fully secure within their environment. Whether deployed in your cloud or VPC, no data leaves your environment, providing complete control and compliance.
- Bolster LLM Security: Monitor data anomalies, biases, and security vulnerabilities in real-time and over time with customizable dashboards and reports for contextual insights on how to make LLM security robust.
By integrating proactive security measures and leveraging Fiddler’s all-in-one AI Observability and Security platform, customers can bring LLM observability, Trust Service, Guardrails, and contextual model analytics into a unified platform. This comprehensive approach ensures AI remains powerful, accurate, and secure, enabling organizations to safeguard their AI investments confidently.
Learn about Fiddler Trust Service to define key LLM metrics for your use case and proactively detect and mitigate harmful, costly risks.
Frequently Asked Questions about LLM Security Risks
1. What are the security concerns of LLMs?
LLMs pose security risks, such as data leaks, prompt injection attacks, adversarial manipulation, and unauthorized access. Without proper safeguards, they can expose sensitive information, generate misleading outputs, or allow malicious actors to exploit them.
2. What is LLM vulnerability?
LLM vulnerabilities refer to weaknesses that attackers can exploit, including insecure API integrations, model poisoning, prompt injections, and remote code execution. These risks can compromise data security, model integrity, and system reliability.
