Understanding the OWASP Top 10 for LLMs: Risks and Controls

1. Prompt Injection

Prompt injection occurs when malicious inputs manipulate a Large Language Model (LLM) into executing unintended actions or revealing sensitive data. Attackers craft inputs that override the model’s instructions, potentially leading to data leaks or unauthorized actions.

Risk: This vulnerability can expose confidential information, like user data or system prompts, and enable attackers to bypass security measures. For instance, an attacker might trick a customer service bot into disclosing backend credentials by phrasing a query to ignore prior instructions.

Controls: Implement strict input validation and sanitization to filter out malicious prompts. Use context-aware guardrails to detect and block attempts to override instructions. Sandboxing the LLM environment can also limit the damage of a successful injection by restricting access to sensitive systems.

2. Insecure Output Handling

LLMs often generate outputs that are directly rendered or executed without proper validation. If an attacker manipulates the output, it could lead to cross-site scripting (XSS) in web applications or even code execution on backend systems.

Risk: Unchecked outputs can inject malicious scripts into a webpage, compromising user sessions or stealing data. In severe cases, outputs processed by downstream systems might trigger unintended commands, escalating to full system compromise.

Controls: Always sanitize and escape LLM outputs before rendering them in a browser or passing them to other systems. Employ allowlists for acceptable content and block executable code in outputs. Regularly monitor for anomalies in output behavior that might indicate exploitation.

3. Training Data Poisoning

LLMs rely on vast datasets for training, and if these datasets are tainted with biased, malicious, or inaccurate data, the model’s behavior can be skewed. Poisoned data can introduce backdoors or degrade the model’s reliability.

Risk: A poisoned model might produce harmful or biased outputs, damaging user trust or enabling targeted attacks. For example, a financial chatbot with poisoned data might consistently recommend fraudulent investments.

Controls: Vet and curate training data sources meticulously. Use anomaly detection to identify and remove malicious data points during training. Regularly audit model outputs for signs of bias or unexpected behavior that could indicate poisoning.

4. Model Denial of Service (DoS)

Attackers can overwhelm an LLM with resource-intensive queries, degrading performance or making the service unavailable to legitimate users. This can also inflate operational costs due to excessive resource consumption.

Risk: A DoS attack can disrupt critical services, like customer support bots, leading to downtime and financial losses. It can also mask other malicious activities happening simultaneously.

Controls: Implement rate limiting and query complexity checks to prevent abuse. Deploy monitoring tools to detect unusual spikes in resource usage. Consider caching frequent queries to reduce load on the model during high-traffic periods.

5. Supply Chain Vulnerabilities

LLMs often depend on third-party datasets, pre-trained models, or plugins. Weaknesses in these components can introduce vulnerabilities, such as outdated libraries or compromised training data.

Risk: A compromised supply chain component can propagate flaws into the LLM, leading to unreliable outputs or security breaches. An attacker exploiting a vulnerable plugin could gain access to the broader system.

Controls: Conduct thorough security assessments of all third-party components. Maintain an inventory of dependencies and monitor for known vulnerabilities using tools like dependency scanners. Limit permissions for external integrations to minimize potential damage.

6. Sensitive Information Disclosure

LLMs may inadvertently reveal sensitive information from their training data or user interactions, especially if not properly configured. This includes personal data, proprietary code, or system details.

Risk: Disclosure of sensitive data can lead to privacy violations, regulatory penalties, or competitive disadvantages. For instance, a healthcare LLM might accidentally leak patient information in its responses.

Controls: Apply data anonymization techniques during training to strip identifiable information. Use fine-tuning to exclude sensitive topics from responses. Implement strict access controls and logging to track data exposure incidents.

7. Insecure Plugin Design

Many LLMs integrate with plugins or APIs to extend functionality, but poorly designed plugins can introduce vulnerabilities. These might include insufficient authentication or excessive privileges.

Risk: A flawed plugin can serve as an entry point for attackers to manipulate the LLM or access connected systems. For example, a plugin with hardcoded credentials could be exploited to exfiltrate data.

Controls: Enforce secure coding practices for plugins, including input validation and least privilege principles. Require strong authentication for plugin interactions. Regularly audit and update plugins to address emerging threats.

8. Excessive Agency

LLMs with too much autonomy or access to external systems can perform unintended actions on behalf of users. This “excessive agency” can amplify the impact of other vulnerabilities like prompt injection.

Risk: An overprivileged LLM might delete critical data, send unauthorized emails, or execute harmful commands. A compromised chatbot with API access could wreak havoc on integrated systems.

Controls: Limit the LLM’s capabilities to only what is necessary for its role. Implement human-in-the-loop validation for high-risk actions. Use read-only access where possible to prevent destructive operations.

9. Overreliance

Organizations or users may place undue trust in LLM outputs without verification, leading to incorrect decisions or actions. LLMs can produce confident but inaccurate responses, often termed “hallucinations.”

Risk: Overreliance can result in operational errors, misinformation, or security lapses. A developer relying on an LLM for code suggestions might deploy vulnerable code without proper review.

Controls: Educate users on the limitations of LLMs and encourage independent validation of outputs. Include disclaimers or confidence scores in responses to signal potential inaccuracy. Design workflows that require human oversight for critical decisions.

10. Model Theft

Attackers may attempt to steal or replicate an LLM by querying it extensively to extract its behavior or training data. This can compromise intellectual property or enable the creation of malicious clones.

Risk: Model theft can erode competitive advantage and lead to misuse of proprietary technology. A stolen model could be repurposed for spreading disinformation or launching targeted attacks.

Controls: Restrict access to the model through authentication and usage quotas. Use watermarking techniques to trace stolen outputs back to the source. Monitor query patterns for signs of systematic extraction attempts.

Navigating the risks associated with LLMs requires a proactive approach to security. By understanding the OWASP Top 10 for LLMs and implementing robust controls, organizations can harness the power of these models while minimizing potential threats. Stay vigilant, keep systems updated, and prioritize security at every layer.