arXiv AI

Mitigating The Effect of Class Imbalance in Data with Hierarchical and Dependable Structure

arXiv:2607. 11994v1 Announce Type: cross Abstract: Classifying cybersecurity vulnerabilities using the Common Weakness Enumeration (CWE) taxonomy is challenging due to extreme class imbalance and strong hierarchical dependencies among weakness categories.

arXiv Machine Learning

Jul 9

Multi-Class vs. Multi-Label BERT for CVE-to-CWE Mapping: How Taxonomy Structure Shapes the Errors

arXiv:2607. 07573v1 Announce Type: new Abstract: Assigning Common Weakness Enumeration (CWE) categories to Common Vulnerabilities and Exposures (CVE) records remains an important but largely manual step in vulnerability analysis.

By Ana Schwengber Kelm, Christian Bockermann, J\"org Frochte

llms nlp

Hugging Face Trending Papers

Jul 8

Multi-Class vs. Multi-Label BERT for CVE-to-CWE Mapping: How Taxonomy Structure Shapes the Errors

Assigning Common Weakness Enumeration (CWE) categories to Common Vulnerabilities and Exposures (CVE) records remains an important but largely manual step in vulnerability analysis. We study this task as a text classification problem and compare two modelling choices: a \emph{multi-class} formulation that predicts a single CWE per CVE and a \emph{multi-label} formulation that allows multiple assignments.

llms nlp

arXiv Machine Learning

Jul 3

From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection

arXiv:2512. 10485v2 Announce Type: replace-cross Abstract: Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness remains underexplored.

By Chaomeng Lu, Bert Lagaisse

llms benchmarks

arXiv Machine Learning

Jul 9

MARGIN: Margin-Aware Regularized Geometry for Imbalanced Vulnerability Detection

arXiv:2605. 10240v3 Announce Type: replace-cross Abstract: Software vulnerability detection is critical for ensuring software security and reliability.

By Yuteng Zhang, Huifang Ma, Jiahui Wei, Qingqing Li, Yafei Yang

rag safety

arXiv Machine Learning

Jun 30

ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data

arXiv:2408. 16028v4 Announce Type: replace-cross Abstract: Supervised-learning-based vulnerability detectors often fall short due to limited labelled training data.

By Weizhou Wang, Eric Liu, Xiangyu Guo, Xiao Hu, Ilya Grishchenko, David Lie

llms benchmarks

arXiv AI

Jun 9

Large Language Models for Imbalanced Classification: Diversity makes the difference

arXiv:2510. 09783v2 Announce Type: replace-cross Abstract: Oversampling is one of the most widely used approaches for addressing imbalanced classification.

By Dang Nguyen, Sunil Gupta, Kien Do, Thin Nguyen, Taylor Braund, Alexis Whitton, Svetha Venkatesh

llms fine-tuning benchmarks

arXiv AI

Jun 18

From Concept-Aligned Tokens to Vulnerable Features: Mechanistic Localization of Jailbreaks

arXiv:2604. 23130v2 Announce Type: replace-cross Abstract: Jailbreak attacks expose a persistent failure mode in safety-aligned LLMs: models can be pushed into harmful behavior, but the internal representations enabling this shift remain poorly localized.

By Nilanjana Das, Mathew Dawit, Aman Chadha, Manas Gaur

llms safety

arXiv Machine Learning

Jul 30

Lilith: Backdoor Generalization under Training-Inference Trigger Shift

arXiv:2607. 26099v1 Announce Type: cross Abstract: Machine-learning services increasingly rely on public data, third-party providers, and outsourced training, creating opportunities for data-poisoning attacks that implant persistent malicious behavior while preserving benign utility.

By Zhou Feng, Jiahao Chen, Chunyi Zhou, Yuan Su, Tianyu Du, Yuwen Pu, Jianhai Chen, Jinbao Li, Shouling Ji

safety

arXiv AI

Jun 29

iCost: A Novel Instance-Complexity-Based Cost-Sensitive Learning Framework

arXiv:2409. 13007v3 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge in classification tasks, often causing standard learning algorithms to become biased toward the majority class.

By Asif Newaz, Asif Ur Rahman Adib, Taskeed Jabid

safety

arXiv AI

Jun 16

Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques

arXiv:2509. 07605v2 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge to supervised classification, particularly in critical domains like medical diagnostics and anomaly detection where minority class instances are rare.

By Ali Nawaz, Amir Ahmad, Shehroz S. Khan

benchmarks safety

arXiv Machine Learning

Jun 16

Data-Centric Benchmarking of Exploit Generation in LLMs: Understanding the Impact of Fine-Tuning

arXiv:2606. 15123v1 Announce Type: cross Abstract: We study the task of CVE-conditioned exploit generation, where a model drafts proof-of-concept (PoC) exploits given software vulnerability context.

By Yiwei Chen, Lichi Li, Kai Cheung, Vinny Parla, Ganesh Sundaram

llms fine-tuning benchmarks data-engineering

arXiv AI

Jul 14

SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification

arXiv:2607. 09936v1 Announce Type: cross Abstract: Cybersecurity systems must adapt rapidly to emerging threats.

By Ivan Alejandro Montoya Sanchez, Anantaa Kotal, Aritran Piplai

llms fine-tuning efficiency benchmarks safety

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