AWS Certified Cloud Practitioner
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A company wants to use a large language model (LLM) to develop a conversational agent. The company needs to prevent the LLM from being manipulated with common prompt engineering techniques to perform undesirable actions or expose sensitive information. Which action will reduce these risks?
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Explanation:
Explanation
Correct Answer: A
Creating a prompt template that teaches the LLM to detect attack patterns is the most effective approach to mitigate prompt injection attacks and manipulation attempts.
Why Option A is Correct:
- Prompt Engineering Defense: A well-designed prompt template can include instructions that help the LLM recognize and reject malicious inputs, such as prompt injection attempts, jailbreak attempts, or attempts to extract sensitive information.
- Proactive Security: This approach addresses the root cause by training the LLM to identify attack patterns rather than just limiting functionality.
- Industry Best Practice: Many organizations implement prompt templates with security guardrails to prevent LLM manipulation.
Why Other Options Are Incorrect:
Option B: Increase the temperature parameter
- Temperature controls randomness in LLM responses (higher = more random, lower = more deterministic)
- Increasing temperature makes responses more varied but doesn't prevent prompt injection attacks
- May actually increase risk by making the LLM more unpredictable
Option C: Avoid using LLMs not listed in Amazon SageMaker
- Amazon SageMaker is an AWS machine learning service, but using SageMaker-listed models doesn't inherently protect against prompt engineering attacks
- The security issue is in how the LLM is used, not which platform hosts it
- Many secure and insecure LLMs can be deployed on various platforms
Option D: Decrease the number of input tokens
- Limiting input tokens may restrict some complex attacks but doesn't address fundamental prompt injection vulnerabilities
- Attackers can craft effective prompt injections within token limits
- This approach reduces functionality without providing meaningful security
Additional Security Recommendations:
- Input Validation: Implement robust input validation and sanitization
- Output Filtering: Filter and monitor LLM outputs for sensitive information
- Rate Limiting: Implement rate limiting to prevent automated attacks
- Monitoring: Continuously monitor for unusual patterns in LLM interactions
- Regular Updates: Keep prompt templates updated as new attack techniques emerge
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