Unveiling AI Agent Vulnerabilities Part V: Securing LLM Services

By Sean Park (Principal Threat Researcher)

Large language models (LLMs) are becoming integral to modern applications, and their security is more critical than ever. Our previous entries discussed the emerging vulnerabilities that threaten AI agents, mainly focusing on key areas such as code execution, data exfiltration, and database access.

In this final article of the series, we explore how to stay ahead of the challenges these threats present and discuss the need for robust, multi-layered strategies to safeguard these systems. Read the rest of our research:

• Part I: Unveiling AI Agent Vulnerabilities: Introduces key security risks in AI agents, such as prompt injection and unauthorized code execution, and outlines the structure for subsequent discussions on data exfiltration, database exploitation, and mitigation strategies.
• Part II: Code Execution Vulnerabilities: Explores how adversaries can exploit weaknesses in LLM-driven services to execute unauthorized code, bypass sandbox restrictions, and exploit flaws in error-handling mechanisms, which lead to data breaches, unauthorized data transfers, and persistent access within execution environments.
• Part III: Data Exfiltration: Examines how adversaries can exploit indirect prompt injection, leveraging multi-modal LLMs like GPT-4o to exfiltrate sensitive data through seemingly benign payloads. This zero-click exploit enables adversaries to embed hidden instructions in web pages, images, and documents, tricking AI agents into leaking confidential information from user interactions, uploaded files, and chat memory.
• Part IV: Database Access Vulnerabilities: Discusses how adversaries exploit LLM-integrated database systems through SQL injection, stored prompt injection, and vector store poisoning to extract restricted data and bypass authentication mechanisms. Attackers can use prompt manipulation to influence query results, retrieve confidential information, or insert persistent exploits that affect future queries.

Mitigating code execution vulnerabilities

The core challenge with code execution vulnerabilities lies in the uncontrolled capacity of LLMs to inadvertently perform undesired actions. By providing AI agents with broad system access, attackers can manipulate the unintentional execution of code. Tackling this requires a foundational shift towards containment. Effective sandboxing that limits what processes can run and what file system areas can be accessed is fundamental, blocking potential freeloaders. Moreover, resource controls, such as throttling CPU and memory, act as a fail-safe. These approaches don’t just reduce the chance of a security breach but actively defend the system from the unintended consequences of a triggerable vulnerability.

At the heart of code execution vulnerabilities is the risk that an LLM might be exploited to perform unauthorized actions if given excessive privileges or unchecked access. The core challenge is minimizing the system's exposure to potentially malicious commands without stifling legitimate operations. One prominent approach is to enforce stringent sandboxing: by isolating processes and limiting file system interactions, we reduce the available pathways for an attacker. Additionally, applying resource limitations, such as capping memory, CPU usage, and execution time, ensures that even if a breach occurs, its impact remains contained. Continuous monitoring further reinforces this defense, allowing early detection of abnormal activities that may signal an attempted exploit.

To address the classes of vulnerabilities, the following proactive security measures are recommended:

1. Restrict system capabilities:
   • Disable background processes or limit them to specific operations
   • Enforce stricter permissions on file system access
2. Activity monitoring:
   • Track account activities, failures, and unusual behavior to identify potential threats
3. Resource limitation:
   • Impose limits on sandbox resource usage (e.g., memory, CPU, execution time) to prevent abuse or exhaustion
4. Internet access control:
   • Control external access from within the sandbox to reduce the attack surface
5. Monitor for malicious activity:
   • Use behavior analysis tools to identify suspicious operations, such as file monitoring and tampering
6. Input validation:
   • Validate and sanitize data in the pipeline in both directions (from user to sandbox and from sandbox to user)
7. Schema enforcement:
   • Ensure all outputs conform to expected formats before passing data downstream

Mitigating data exfiltration vulnerabilities

Data exfiltration vulnerabilities hinge on the unintended movement of sensitive or confidential information outside the bounds of a system. Obscured prompts or hidden injections, not especially apparent within regular inputs, can lead to data leakage that could severely compromise organizations. Combatting this requires a dual-layered methodology that isolates potential threats and decodes hidden malicious instructions embedded within benign-looking data. Enabling network-level isolation between trusted and untrusted entities offers an upfront defense, while validated input checking disarms hidden prompt injections. Critical to this is the use of advanced diagnostic tools, like optical character recognition and contextual behavior analysis, to reveal and neutralize potential exfiltration activities long before they relapse into full-blown breaches.

Data exfiltration vulnerabilities often emerge from the ability to subtly inject and manipulate prompts in a way that leads to unintended data leaks. The insight here is that the system’s interactions with external inputs need to be as tightly controlled as its internal processes. One effective strategy is to isolate the LLM from untrusted external sources using network segmentation and strict access controls, thereby creating a robust barrier against unauthorized data flows. Complementing this, advanced inspection techniques, such as enhanced payload analysis and automated content moderation, help identify hidden or obfuscated threats within incoming data. By coupling these methods with comprehensive logging and behavior monitoring, we can quickly spot anomalies that may indicate data exfiltration attempts and respond before significant damage occurs.

To address indirect prompt injection risks, a multi-faceted strategy is essential. Key proactive security measures include:

1. Access control and isolation
   • Block untrusted URLs using network-level controls
2. Payload inspection
   • Use advanced filtering to scan uploads for hidden instructions
3. Content moderation and prompt sanitization
   • Detect and neutralize embedded instructions with moderation pipelines and threat detection models
   • Sanitize input data to remove or isolate malicious prompts
4. Enhanced logging and monitoring
   • Log interactions and monitor for unusual LLM output patterns to identify threats

Mitigating database access vulnerabilities

Database access vulnerabilities exploit the inherent difficulty LLMs have in distinguishing between benign and malicious instructions, particularly in scenarios involving prompt injection. The challenge lies in preventing unauthorized commands from reaching critical data stores. To tackle this, it is crucial to move beyond traditional sanitization methods. A robust defense is built on a multi-layered strategy that includes verification protocols, such as requiring confirmation steps for sensitive operations, and intent-based filtering, which evaluates the purpose behind each command. Establishing strict boundaries between the LLM and the database further limits the risk, ensuring that only predefined, safe operations are permitted. This combination of proactive verification and stringent access control forms a comprehensive barrier against potential injection attacks.

Mitigating this class of vulnerabilities, particularly SQL generation vulnerabilities and vector store poisoning, is inherently challenging due to the root cause — LLMs' susceptibility to prompt injection and their inability to reliably discern malicious intent. As prompt injection techniques continue to evolve, a multi-layered approach is essential to reduce the risk. Key proactive security recommendations include:

1. Traditional data sanitization and filtering
   • While traditional techniques for cleaning and filtering user input are helpful, their coverage is inherently limited, especially against sophisticated or obfuscated prompt injection attempts.
2. Verification prompts
   • Implementing verification steps, such as intermediate prompts for confirming critical actions, can help prevent LLMs from executing unintended commands or accessing unauthorized data.
3. Intent classification
   • Using intent classification models to detect and block malicious inputs is particularly effective for stored prompt injection attacks. These models can identify potentially harmful or irrelevant inputs before they reach the LLM or database.
4. LLM-to-database access control
   • Enforcing strict access controls between the LLM and the database can mitigate SQL generation vulnerabilities by ensuring that LLMs can only access or modify data within predefined boundaries. This helps prevent unauthorized queries or modifications.

Conclusion

In today’s dynamic digital landscape, securing AI agents is not just an option — it’s a necessity. The evolving threats surrounding code execution, data exfiltration, and database access remind us that a proactive, multi-layered defense strategy is key. By integrating secure sandboxing and stringent resource management, we can reduce the risks of unauthorized code execution. Similarly, advanced payload analysis and strict network isolation help safeguard our systems against subtle data exfiltration. Finally, moving beyond basic sanitization to adopt verification protocols and intent-based filtering ensures that only trusted, pre-approved operations interact with our databases.

As the methods used by attackers continue to evolve, so must our defenses. Embracing these robust security practices not only protects our AI-driven systems but also builds a resilient foundation for future innovations. Let’s continue to push for proactive security measures like continuous monitoring, adaptive strategies, and rigorous security protocols to keep pace with the challenges of tomorrow.