The Security Budget of Code
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The Security Budget of Code LLMs: An Information-Theoretic Capacity-Security Bound
Announce Type: new Abstract: AI programming assistants make natural-language prompts a software-development interface, so small prompt perturbations become usability and security risks. We study an information-theoretic trade-off for code LLMs between functional capacity, $\Cap=\rmI(c^*;c_\pi)$, and perturbation retention, $\Sec=\rmI(c_\pi;\tilde c_\pi)$. Here $\Sec$ is a retention-channel quantity, not a direct measure of exploit success or vulnerable-code generation. For code completion...
The Security Budget of Code LLMs: An Information-Theoretic Capacity-Security Bound
arXiv:2606.03308v2 Announce Type: replace Abstract: AI programming assistants make natural-language prompts a software-development interface, so small prompt perturbations become usability and security risks. We study an information-theoretic trade-off for code LLMs between functional capacity, $\Cap=\rmI(c^*;c_\pi)$, and perturbation retention, $\Sec=\rmI(c_\pi;\tilde c_\pi)$. Here $\Sec$ is a retention-channel quantity, not a direct measure of exploit success or vulnerable-code generation....
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SecRL-Prune: Structured Reinforcement Learning-Based Pruning of CodeLLMs for Preserving Adversarial Code Mutation
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