Vulnerability Detection via Topological Analysis of Attention Maps
Recently, deep learning (DL) approaches to vulnerability detection have gained significant traction. These methods demonstrate promising results, often surpassing traditional static code analysis tools in effectiveness. In this study, we explore a novel approach to vulnerability detection utilizing the tools from topological data analysis (TDA) on the attention matrices of the BERT model. Our findings reveal that traditional machine learning (ML) techniques, when trained on the topological features extracted from these attention matrices, can perform competitively with pre-trained language models (LLMs) such as CodeBERTa. This suggests that TDA tools, including persistent homology, are capable of effectively capturing semantic information critical for identifying vulnerabilities.
Code (1)
Tasks
Topological Data AnalysisVulnerability DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Bridging Semantics & Structure for Software Vulnerability Detection using Hybrid Network Models
Software vulnerabilities remain a persistent risk, yet static and dynamic analyses often overlook structural dependencies that shape insecure behaviors. Viewing programs as heterogeneous graphs, we capture control- and d…
Vulnerability DetectionBinary ClassificationAcceptability Judgements via Examining the Topology of Attention Maps
The role of the attention mechanism in encoding linguistic knowledge has received special interest in NLP. However, the ability of the attention heads to judge the grammatical acceptability of a sentence has been underex…
CoLALinguistic AcceptabilitySentenceTopological Data AnalysisUncertainty Estimation of Transformers' Predictions via Topological Analysis of the Attention Matrices
Transformer-based language models have set new benchmarks across a wide range of NLP tasks, yet reliably estimating the uncertainty of their predictions remains a significant challenge. Existing uncertainty estimation (U…
text-classificationText ClassificationText DetectionTopological Data AnalysisArtificial Text Detection via Examining the Topology of Attention Maps
The impressive capabilities of recent generative models to create texts that are challenging to distinguish from the human-written ones can be misused for generating fake news, product reviews, and even abusive content. …
Text DetectionTopological Data AnalysisHalluZig: Hallucination Detection using Zigzag Persistence
The factual reliability of Large Language Models (LLMs) remains a critical barrier to their adoption in high-stakes domains due to their propensity to hallucinate. Current detection methods often rely on surface-level si…