CoT prompting in LLM4Code shows mixed robustness that depends on model family, task structure, and perturbations destabilizing structural anchors, leading to trajectory deformations like lengthening, branching, and simplification.
Instruction tuning for secure code generation
6 Pith papers cite this work. Polarity classification is still indexing.
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QuantGuard uses differentiable rounding control to break quantization-boundary backdoors in LLMs, reducing post-quantization attack success to clean-model levels across six models and INT8/FP4/NF4.
Acoda uses a genetic algorithm to optimize eight obfuscation methods that reduce LLM code analysis success rates to as low as 30% while preserving original semantics.
SecureForge audits LLM code for vulnerabilities, builds a synthetic prompt corpus via Markovian sampling, and optimizes system prompts to cut security issues by up to 48% while preserving unit test performance, with zero-shot transfer to real prompts.
The paper releases a 1,554-prompt consensus-labeled bank separating executable malicious code requests from security knowledge requests, validated by five-model majority labeling with Fleiss' kappa of 0.876.
A study protocol proposing a balanced crossover experiment to test whether LLM assistance in vulnerability patching accelerates fixes or introduces superficial insecure patches that pass functional but fail security validation.
citing papers explorer
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Structural Anchors and Reasoning Fragility:Understanding CoT Robustness in LLM4Code
CoT prompting in LLM4Code shows mixed robustness that depends on model family, task structure, and perturbations destabilizing structural anchors, leading to trajectory deformations like lengthening, branching, and simplification.
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Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors
QuantGuard uses differentiable rounding control to break quantization-boundary backdoors in LLMs, reducing post-quantization attack success to clean-model levels across six models and INT8/FP4/NF4.
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Acoda: Adversarial Code Obfuscation for Defending against LLM-based Analysis
Acoda uses a genetic algorithm to optimize eight obfuscation methods that reduce LLM code analysis success rates to as low as 30% while preserving original semantics.
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SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization
SecureForge audits LLM code for vulnerabilities, builds a synthetic prompt corpus via Markovian sampling, and optimizes system prompts to cut security issues by up to 48% while preserving unit test performance, with zero-shot transfer to real prompts.
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A Validated Prompt Bank for Malicious Code Generation: Separating Executable Weapons from Security Knowledge in 1,554 Consensus-Labeled Prompts
The paper releases a 1,554-prompt consensus-labeled bank separating executable malicious code requests from security knowledge requests, validated by five-model majority labeling with Fleiss' kappa of 0.876.
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Helpful or Harmful? Evaluating LLM-Assisted Vulnerability Patching via a Human Study
A study protocol proposing a balanced crossover experiment to test whether LLM assistance in vulnerability patching accelerates fixes or introduces superficial insecure patches that pass functional but fail security validation.