REVIEW 6 cited by
The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models
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
The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models
read the original abstract
Static analysis is a widely used technique in software engineering for identifying and mitigating bugs. However, a significant hurdle lies in achieving a delicate balance between precision and scalability. Large Language Models (LLMs) offer a promising alternative, as recent advances demonstrate remarkable capabilities in comprehending, generating, and even debugging code. Yet, the logic of bugs can be complex and require sophisticated reasoning and a large analysis scope spanning multiple functions. Therefore, at this point, LLMs are better used in an assistive role to complement static analysis. In this paper, we take a deep dive into the open space of LLM-assisted static analysis, using use-before-initialization (UBI) bugs as a case study. To this end, we develop LLift, a fully automated framework that interfaces with both a static analysis tool and an LLM. By carefully designing the framework and the prompts, we are able to overcome a number of challenges, including bug-specific modeling, the large problem scope, the non-deterministic nature of LLMs, etc. Tested in a real-world scenario analyzing nearly a thousand potential UBI bugs produced by static analysis, LLift demonstrates a potent capability, showcasing a reasonable precision (50%) and appearing to have no missing bugs. It even identified 13 previously unknown UBI bugs in the Linux kernel. This research paves the way for new opportunities and methodologies in using LLMs for bug discovery in extensive, real-world datasets.
Forward citations
Cited by 6 Pith papers
-
QASecClaw: A Multi-Agent LLM Approach for False Positive Reduction in Static Application Security Testing
A multi-agent LLM system cuts false positives in static application security testing by 88.6% on the OWASP Benchmark while dropping recall by only 3.1%.
-
Feedback-Driven Execution for LLM-Based Binary Analysis
FORGE uses a reasoning-action-observation loop and Dynamic Forest of Agents to perform scalable LLM-based binary analysis, finding 1,274 vulnerabilities across 591 of 3,457 real-world firmware binaries at 72.3% precis...
-
CodeCureAgent: Automatic Classification and Repair of Static Analysis Warnings
CodeCureAgent achieves 96.8% plausible fixes and 86.3% correct fixes for 1,000 SonarQube warnings across 106 Java projects using an agentic LLM framework.
-
FirmPilot: Evidence-Guided Multi-Agent Environment Recovery for IoT Firmware Rehosting
An evidence-guided multi-agent rehosting loop raises web-service reachability on the LFwC corpus from 25.49% to 52.39% and network reachability from 39.30% to 71.93%.
-
A Survey on Large Language Model based Autonomous Agents
A survey of LLM-based autonomous agents that proposes a unified framework for their construction and reviews applications in social science, natural science, and engineering along with evaluation methods and future di...
-
Using LLMs to Adjudicate Static-Analysis Alerts with Error Reduction Techniques
Mid-tier reasoning LLMs with consistency checks and LLM reasoning evaluation adjudicate static-analysis alerts at ≥98% recall and ≥94.8% specificity across Juliet, FormAI, and SV-COMP.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.