DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models

• URL: http://arxiv.org/abs/2402.13291v2
• Date: Fri, 23 Feb 2024 17:26:06 GMT
• Title: DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models
• Authors: Berkay Berabi, Alexey Gronskiy, Veselin Raychev, Gishor Sivanrupan, Victor Chibotaru, Martin Vechev
• Abstract summary: Large language models (LLMs) are increasingly used to solve various programming tasks. We show that the task is difficult as it requires the model to learn long-range code relationships. We propose a technique to address these challenges with a new approach for querying and fine-tuning LLMs.
• Score: 3.1690235522182104
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The automated program repair field has attracted substantial interest over the years, but despite significant research efforts, creating a system that works well for complex semantic bugs such as security vulnerabilities has proven difficult. A promising direction to solve this challenge is by leveraging large language models (LLMs), which are increasingly used to solve various programming tasks. In this paper, we investigate the effectiveness of LLMs for solving code-repair task. We show that the task is difficult as it requires the model to learn long-range code relationships, a task that inherently relies on extensive amounts of training data. At the same time, creating a large, clean dataset for complex program bugs and their corresponding fixes is non-trivial. We propose a technique to address these challenges with a new approach for querying and fine-tuning LLMs. The idea is to use program analysis to limit the LLM's attention mechanism on the portions of code needed to perform the fix, drastically reducing the amount of required training data. Concretely, for training and inference, rather than feeding the entire program to the LLM, we reduce its code to a much shorter snippet that contains the reported defect together with the necessary context - and use that instead. Our evaluation shows that this code reduction approach substantially improves available models such as GPT-4 using few-shot learning, as well as fine-tuning models. To train and evaluate our system, we created a comprehensive code fixing dataset by extensively labeling 156 bug patterns (including 40 security rules), requiring complex interprocedural dataflow to discover. Our best system with Mixtral-8x7B can remove more than 80% of the reported defects while exactly matching the human fix in between 10 and 50% of cases, outperforming baselines based on GPT-3.5 and GPT-4, or based on window-based models like TFix.

Related papers

• Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification [52.095460362197336]
  Large language models (LLMs) struggle with consistent and accurate reasoning. LLMs are trained primarily on correct solutions, reducing their ability to detect and learn from errors. We propose a novel collaborative method integrating Chain-of-Thought (CoT) and Program-of-Thought (PoT) solutions for verification.
  arXiv  Detail & Related papers  (2024-10-05T05:21:48Z)
• Enhancing Fault Localization Through Ordered Code Analysis with LLM Agents and Self-Reflection [8.22737389683156]
  Large Language Models (LLMs) offer promising improvements in fault localization by enhancing code comprehension and reasoning. We introduce LLM4FL, a novel LLM-agent-based fault localization approach that integrates SBFL rankings with a divide-and-conquer strategy. Our results demonstrate that LLM4FL outperforms AutoFL by 19.27% in Top-1 accuracy and surpasses state-of-the-art supervised techniques such as DeepFL and Grace.
  arXiv  Detail & Related papers  (2024-09-20T16:47:34Z)
• An Empirical Study on Self-correcting Large Language Models for Data Science Code Generation [1.335664823620186]
  Large Language Models (LLMs) have recently advanced many applications on software engineering tasks. CoT-SelfEvolve iteratively and automatically refines code through a self-correcting process.
  arXiv  Detail & Related papers  (2024-08-28T09:19:09Z)
• SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models [54.78329741186446]
  We propose a novel paradigm that uses a code-based critic model to guide steps including question-code data construction, quality control, and complementary evaluation. Experiments across both in-domain and out-of-domain benchmarks in English and Chinese demonstrate the effectiveness of the proposed paradigm.
  arXiv  Detail & Related papers  (2024-08-28T06:33:03Z)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
  Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions. We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types. We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions [72.56339136017759]
  We introduce BigCodeBench, a benchmark that challenges Large Language Models (LLMs) to invoke multiple function calls as tools from 139 libraries and 7 domains for 1,140 fine-grained tasks. Our evaluation shows that LLMs are not yet capable of following complex instructions to use function calls precisely, with scores up to 60%, significantly lower than the human performance of 97%. We propose a natural-language-oriented variant of BigCodeBench, BigCodeBench-Instruct, that automatically transforms the original docstrings into short instructions only with essential information.
  arXiv  Detail & Related papers  (2024-06-22T15:52:04Z)
• Validating LLM-Generated Programs with Metamorphic Prompt Testing [8.785973653167112]
  Large Language Models (LLMs) are increasingly integrated into the software development lifecycle. This paper proposes a novel solution called metamorphic prompt testing to address these challenges. Our evaluation on HumanEval shows that metamorphic prompt testing is able to detect 75 percent of the erroneous programs generated by GPT-4, with a false positive rate of 8.6 percent.
  arXiv  Detail & Related papers  (2024-06-11T00:40:17Z)
• A Comprehensive Study of the Capabilities of Large Language Models for Vulnerability Detection [9.422811525274675]
  Large Language Models (LLMs) have demonstrated great potential for code generation and other software engineering tasks. Vulnerability detection is of crucial importance to maintaining the security, integrity, and trustworthiness of software systems. Recent work has applied LLMs to vulnerability detection using generic prompting techniques, but their capabilities for this task and the types of errors they make remain unclear.
  arXiv  Detail & Related papers  (2024-03-25T21:47:36Z)
• Neuron Patching: Semantic-based Neuron-level Language Model Repair for Code Generation [32.178931149612644]
  ulModel ulImprovement via ulNeuron ulTargeting (textscMINT) is a novel approach for repairing code Language Models (LMs) textscMINT is effective, efficient, and reliable, capable of correcting a neural model by patching a minimum number of neurons.
  arXiv  Detail & Related papers  (2023-12-08T20:28:08Z)
• Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning [70.65666982566655]
  Permutation flow shop scheduling (PFSS) is widely used in manufacturing systems. We propose to train the model via expert-driven imitation learning, which accelerates convergence more stably and accurately. Our model's network parameters are reduced to only 37% of theirs, and the solution gap of our model towards the expert solutions decreases from 6.8% to 1.3% on average.
  arXiv  Detail & Related papers  (2022-10-31T09:46:26Z)
• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning [92.36705236706678]
  "CodeRL" is a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning. During inference, we introduce a new generation procedure with a critical sampling strategy. For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives.
  arXiv  Detail & Related papers  (2022-07-05T02:42:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.