Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy (2410.09102v1)

Abstract: LLMs are susceptible to security and safety threats, such as prompt injection, prompt extraction, and harmful requests. One major cause of these vulnerabilities is the lack of an instruction hierarchy. Modern LLM architectures treat all inputs equally, failing to distinguish between and prioritize various types of instructions, such as system messages, user prompts, and data. As a result, lower-priority user prompts may override more critical system instructions, including safety protocols. Existing approaches to achieving instruction hierarchy, such as delimiters and instruction-based training, do not address this issue at the architectural level. We introduce the Instructional Segment Embedding (ISE) technique, inspired by BERT, to modern LLMs, which embeds instruction priority information directly into the model. This approach enables models to explicitly differentiate and prioritize various instruction types, significantly improving safety against malicious prompts that attempt to override priority rules. Our experiments on the Structured Query and Instruction Hierarchy benchmarks demonstrate an average robust accuracy increase of up to 15.75% and 18.68%, respectively. Furthermore, we observe an improvement in instruction-following capability of up to 4.1% evaluated on AlpacaEval. Overall, our approach offers a promising direction for enhancing the safety and effectiveness of LLM architectures.

Instructional Segment Embedding: Enhancing LLMs' Safety with Instruction Hierarchy

The paper introduces an innovative approach termed Instructional Segment Embedding (ISE), focusing on fortifying the security and safety of LLMs against prevalent vulnerabilities such as prompt injection, prompt extraction, and processing of harmful requests. Traditional LLM architectures inadequately handle instruction hierarchy, treating all inputs equivalently. This oversight can permit malicious user prompts to override critical system instructions, compromising safety protocols.

Key Contributions

1. Identification of Hierarchy Limitations: The paper identifies critical shortcomings in existing LLM architectures concerning their inability to recognize and prioritize instruction hierarchies, leading to susceptibility to various prompt-based attacks.
2. Proposal of ISE: ISE is introduced to encode instruction priority directly into the model, enabling enhanced differentiation and prioritization of instruction types. This is achieved by augmenting input tokens with segment information, categorizing them into system instructions, user prompts, and data inputs.
3. Empirical Validation: Extensive experiments on benchmarks such as Structured Query and Instruction Hierarchy reveal that ISE significantly increases robustness—up to a 15.75% improvement in robust accuracy against indirect prompt injection and up to an 18.68% boost across multiple vulnerabilities.

Architecture and Implementation

ISE involves the integration of a segment embedding layer trained alongside other model parameters. Tokens are tagged according to their hierarchical relevance, and these tags are processed through an embedding layer that directly informs the model's self-attention mechanisms, leading to improved robustness and instruction-following capabilities.

Experimental Findings

The paper provides comprehensive evaluations across multiple LLMs, including Llama-2-13B and Llama-3-8B. Results illustrate consistent improvements in both clean and adversarial settings:

• Structured Query Benchmark: ISE maintains high utility in model capability (72.13% win rate on AlpacaEval) and surpasses previous robustness strategies against prompt injections.
• Instruction Hierarchy Benchmark: The ISE method enhances performance in handling indirect/direct prompt injection, prompt extraction, and harmful requests across various datasets, demonstrating robustness improvements up to 25% for some models.

Implications and Future Directions

ISE's introduction provides a promising method for reinforcing instruction hierarchy within LLMs. Its simplicity and compatibility with existing architectures suggest extensive applicability across future models. However, the approach primarily addresses non-adaptive jailbreak attacks; further integration with adversarial training strategies could enhance defense against such adaptive threats. Future research could explore applying ISE during pre-training or in multi-turn conversation contexts to generalize its efficacy further.

In conclusion, this paper offers a methodologically sound and empirically validated approach to bolstering LLM safety. The potential integration of ISE into real-world applications, alongside traditional adversarial defenses, could significantly advance the reliable deployment of AI systems.