REVIEW 5 cited by
Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerability detection and repair. Numerous studies have investigated the application of LLMs to enhance vulnerability detection and repair tasks. Despite the increasing research interest, there is currently no existing survey that focuses on the utilization of LLMs for vulnerability detection and repair. In this paper, we aim to bridge this gap by offering a systematic literature review of approaches aimed at improving vulnerability detection and repair through the utilization of LLMs. The review encompasses research work from leading SE, AI, and Security conferences and journals, encompassing 43 papers published across 25 distinct venues, along with 15 high-quality preprint papers, bringing the total to 58 papers. By answering three key research questions, we aim to (1) summarize the LLMs employed in the relevant literature, (2) categorize various LLM adaptation techniques in vulnerability detection, and (3) classify various LLM adaptation techniques in vulnerability repair. Based on our findings, we have identified a series of limitations of existing studies. Additionally, we have outlined a roadmap highlighting potential opportunities that we believe are pertinent and crucial for future research endeavors.
Forward citations
Cited by 5 Pith papers
-
DREA: Decoupled Reasoning and Exploration Agents for Repository-Level Vulnerability Detection
DREA improves repository-level vulnerability detection by coupling an LLM planner that forms hypotheses with a cheap local explorer that gathers cross-file evidence, lifting paired accuracy from 19-26% to 30-42% at mu...
-
Boosting Vulnerability Detection of LLMs via Curriculum Preference Optimization with Synthetic Reasoning Data
ReVD uses synthetic vulnerability reasoning data and curriculum preference optimization to boost LLM vulnerability detection accuracy by 12-22% over prior baselines on PrimeVul and SVEN.
-
Eradicating the Unseen: Detecting, Exploiting, and Remediating a Path Traversal Vulnerability across GitHub
A single vulnerable Node.js path traversal pattern was found in 1,756 GitHub projects, most rated critical, and the authors' automated pipeline produced patches, disclosures, and evidence that LLMs have learned the pattern.
-
Analyzing the Instability of Large Language Models in Automated Bug Injection and Correction
GPT-4's bug fixes on QuixBugs vary across repeated runs, and increasing temperature from 0 to 1 lowers both syntactic similarity and the rate of passing tests.
-
VulBinLLM: LLM-powered Vulnerability Detection for Stripped Binaries
An LLM pipeline that enriches decompiled code with vulnerability hints and then classifies CWEs reports high Juliet accuracy, but leaky hints and unmatched baselines weaken the claim.
Discussion (0). Sign in to comment.