A Content-Based Framework for Cybersecurity Refusal Decisions in Large Language Models
arXiv:2602.15689v1 Announce Type: new Abstract: Large language models and LLM-based agents are increasingly used for cybersecurity tasks that are inherently dual-use. Existing approaches to refusal, spanning academic policy frameworks and commercially deployed systems, often rely on broad topic-based bans or offensive-focused taxonomies. As a result, they can yield inconsistent decisions, over-restrict legitimate defenders, and behave brittlely under obfuscation or request segmentation. We argue that effective refusal requires explicitly modeling the trade-off between offensive risk and defensive benefit, rather than relying solely on intent or offensive classification. In this paper, we introduce a content-based framework for designing and auditing cyber refusal policies that makes offense-defense tradeoffs explicit. The framework characterizes requests along five dimensions: Offensive Action Contribution, Offensive Risk, Technical Complexity, Defensive Benefit, and Expected Frequenc
arXiv:2602.15689v1 Announce Type: new Abstract: Large language models and LLM-based agents are increasingly used for cybersecurity tasks that are inherently dual-use. Existing approaches to refusal, spanning academic policy frameworks and commercially deployed systems, often rely on broad topic-based bans or offensive-focused taxonomies. As a result, they can yield inconsistent decisions, over-restrict legitimate defenders, and behave brittlely under obfuscation or request segmentation. We argue that effective refusal requires explicitly modeling the trade-off between offensive risk and defensive benefit, rather than relying solely on intent or offensive classification. In this paper, we introduce a content-based framework for designing and auditing cyber refusal policies that makes offense-defense tradeoffs explicit. The framework characterizes requests along five dimensions: Offensive Action Contribution, Offensive Risk, Technical Complexity, Defensive Benefit, and Expected Frequency for Legitimate Users, grounded in the technical substance of the request rather than stated intent. We demonstrate that this content-grounded approach resolves inconsistencies in current frontier model behavior and allows organizations to construct tunable, risk-aware refusal policies.
Executive Summary
The article introduces a content-based framework for cybersecurity refusal decisions in large language models (LLMs), addressing the limitations of current approaches that rely on broad topic-based bans or offensive-focused taxonomies. The proposed framework explicitly models the trade-off between offensive risk and defensive benefit, characterizing requests along five dimensions: Offensive Action Contribution, Offensive Risk, Technical Complexity, Defensive Benefit, and Expected Frequency for Legitimate Users. The authors argue that this content-grounded approach resolves inconsistencies in current model behavior and allows for the construction of tunable, risk-aware refusal policies.
Key Points
- ▸ Current refusal approaches in LLMs are inconsistent and brittle.
- ▸ A content-based framework is proposed to model offense-defense tradeoffs.
- ▸ The framework characterizes requests along five technical dimensions.
- ▸ The approach resolves inconsistencies and allows for tunable refusal policies.
Merits
Comprehensive Framework
The framework provides a detailed and structured approach to evaluating cybersecurity refusal decisions, making it more robust and consistent compared to existing methods.
Technical Grounding
By focusing on the technical substance of requests rather than stated intent, the framework offers a more reliable basis for decision-making.
Tunable Policies
The framework allows organizations to construct refusal policies that can be adjusted based on specific risk tolerances and defensive needs.
Demerits
Complexity
The framework's complexity may make it difficult to implement and maintain, requiring significant expertise and resources.
Potential Over-Restriction
Despite efforts to minimize over-restriction, there is still a risk that legitimate defensive uses could be inadvertently restricted.
Scalability
The scalability of the framework across different types of LLMs and cybersecurity tasks remains to be thoroughly tested.
Expert Commentary
The article presents a significant advancement in the field of AI-driven cybersecurity, addressing a critical gap in current refusal policies. The proposed content-based framework offers a more nuanced and technically grounded approach to evaluating cybersecurity refusal decisions, which is crucial given the dual-use nature of many cybersecurity tasks. The framework's emphasis on modeling offense-defense tradeoffs is particularly noteworthy, as it provides a structured method for balancing the risks and benefits of different requests. However, the complexity of the framework may pose implementation challenges, and further research is needed to validate its scalability and effectiveness across diverse scenarios. Overall, the article makes a valuable contribution to the ongoing dialogue on AI ethics and cybersecurity policy, offering practical insights for both academia and industry.
Recommendations
- ✓ Further empirical studies should be conducted to validate the framework's effectiveness and scalability in real-world cybersecurity scenarios.
- ✓ Organizations should consider piloting the framework in controlled environments before full-scale implementation to assess its practical benefits and limitations.
