Safeguarding Generative AI: Combatting Indirect Prompt Injections with Amazon Bedrock

Ensuring Security and Reliability in AI Interactions

Understanding Indirect Prompt Injection: Risks and Implications

Strategies for Mitigating Indirect Prompt Injections in Amazon Bedrock Agents

Effective Security Measures Against Indirect Prompt Injections

Conclusion: Integrating Security as a Fundamental Component in AI Systems

About the Authors: Expertise in AI Security at Amazon

Safeguarding AI Interactions: Understanding and Mitigating Indirect Prompt Injections with Amazon Bedrock

Generative AI tools have revolutionized the way we work, create, and process information. As organizations integrate these advancements into critical workflows, security must remain a top priority. At Amazon Web Services (AWS), Amazon Bedrock stands out by providing comprehensive security controls and best practices to protect your applications and data. In this blog post, we’ll explore the security measures and practical strategies that Amazon Bedrock Agents offer to safeguard against indirect prompt injections, ensuring that your AI interactions remain secure and reliable.

What Are Indirect Prompt Injections?

Indirect prompt injections pose a significant cybersecurity risk, distinct from direct prompt injections, which explicitly manipulate an AI system. Indirect prompt injections embed hidden instructions within seemingly innocuous external content—such as documents, emails, or websites. When an unsuspecting user requests their AI assistant to summarize this content, concealed prompts may hijack the AI’s behavior, leading to various security threats, including data exfiltration, misinformation, or bypassing established security controls.

As businesses increasingly pivot to generative AI tools like Amazon Bedrock for enterprise applications, understanding and mitigating the risks associated with indirect prompt injections is crucial to maintaining security and trust in AI systems.

Understanding Indirect Prompt Injection Challenges

The term "prompt injection" derives from SQL injection because both exploit vulnerabilities from concatenating trusted application code with untrusted user input. Indirect prompt injections exploit this by allowing Large Language Models (LLMs) to process untrusted content from external sources controlled by malicious actors or compromised trusted sources.

When a query is submitted, the LLM retrieves content through either direct API calls or systems such as Retrieval Augmented Generation (RAG). During inference, if the model processes malicious instructions embedded in the content, it may lead to various risks:

• System Manipulation: Triggering unauthorized workflows.
• Unauthorized Data Exfiltration: Extracting sensitive information.
• Remote Code Execution: Running harmful code.

The challenge lies in the fact that the malicious prompts may not be visible to users, often hidden using techniques like invisible characters, translucent text, or inconspicuous formatting.

Effectively Safeguarding Against Indirect Prompt Injections

To fortify against indirect prompt injections, Amazon Bedrock Agents provide multiple vectors that must be secured. Here are key strategies to consider:

1. User Confirmation

Implementing user confirmation before executing actions can safeguard the tool input vector. By enabling user approval for mutative actions, developers can ensure that no unauthorized actions take place without explicit consent. This additional oversight provides an essential layer of detection and response against potential prompt injections.

2. Content Moderation with Amazon Bedrock Guardrails

Amazon Bedrock Guardrails offer configurable safeguarding mechanisms that filter out denied topics and redact sensitive information. This dual-layer moderation ensures that both user inputs and model responses are screened, capturing malicious content effectively at multiple checkpoints. Tagging dynamically generated prompts as user input ensures comprehensive evaluation for hidden adversarial instructions.

3. Secure Prompt Engineering

Designing system prompts with a focus on security can improve the robustness of LLM responses. Crafting prompts that instruct LLMs to identify and avoid malicious injections significantly mitigates exposure to indirect prompt injection exploits. Utilizing unique tokens to delimit data boundaries within prompts helps the LLM contextualize inputs while maintaining focus on security.

4. Implementing Verifiers via Custom Orchestration

Custom orchestration in Amazon Bedrock allows developers to establish specific logic tailored to their use case, including verification steps for unexpected tool invocations. By leveraging orchestration strategies like plan-verify-execute (PVE), agents can ensure that only predetermined actions are executed.

5. Access Control and Sandboxing

Applying the principle of least privilege restricts agents and tools to only the resources necessary for their functions. Robust sandboxing of untrusted content is essential in preventing unauthorized access to sensitive actions. Establishing validation layers between content processing and execution enforces security boundaries, complicating the exploitability of compromised agents.

6. Monitoring and Logging

Developing comprehensive monitoring and logging systems enables early detection of suspicious activities. Patterns like unusual query volumes or repetitive prompts should trigger alerts that allow security teams to respond swiftly. Maintaining an audit trail of all inputs and outputs establishes accountability and helps identify sources of security incidents.

7. Standard Application Security Controls

Implement other standard security practices, including rigorous authentication and authorization checks. Ensure knowledge bases contain only information from trusted sources. Regularly testing the integrity of these sources through content sampling can further bolster your defenses.

Conclusion

In this post, we’ve detailed strategies that serve as a multifaceted approach to protecting your Amazon Bedrock Agents from indirect prompt injections. By employing a layered defense—characterized by secure prompt engineering, user confirmation, effective content moderation, and vigilant monitoring—you can significantly reduce vulnerabilities.

Security is not a one-time implementation but an ongoing commitment to evolving threats. By integrating these measures early in the design phase of your AI architecture, you can ensure that Amazon Bedrock Agents operate securely while harnessing the generative AI capabilities that drive value for your organization.

About the Authors

• Hina Chaudhry: Sr. AI Security Engineer at Amazon, Hina safeguards generative AI applications and ensures they meet elevated security standards.

• Manideep Konakandla: Senior AI Security Engineer dedicated to securing Amazon’s generative AI tools, bringing over a decade of security experience.

• Satveer Khurpa: Sr. WW Specialist Solutions Architect for Amazon Bedrock, specializing in designing innovative and secure AI solutions.

• Sumanik Singh: Software Developer Engineer at AWS, contributing to Amazon Bedrock Agents.

By implementing these comprehensive measures and maintaining continuous vigilance, you can confidently deploy AI-powered applications that serve your users’ needs while ensuring rigorous security and compliance. The future of AI relies not just on innovation, but on our unwavering commitment to secure practices.