Enhancing Continual Learning for Software Vulnerability Prediction: Addressing Catastrophic Forgetting via Hybrid-Confidence-Aware Selective Replay for Temporal LLM Fine-Tuning

• URL: http://arxiv.org/abs/2602.23834v1
• Date: Fri, 27 Feb 2026 09:13:23 GMT
• Title: Enhancing Continual Learning for Software Vulnerability Prediction: Addressing Catastrophic Forgetting via Hybrid-Confidence-Aware Selective Replay for Temporal LLM Fine-Tuning
• Authors: Xuhui Dou, Hayretdin Bahsi, Alejandro Guerra-Manzanares,
• Abstract summary: This paper investigates fine-tuning of a decoder-style language model (phi/phi-2 with LoRA) on a CVE-linked dataset.<n>We evaluate eight continual learning strategies, including window-only and cumulative training, replay-based baselines and regularisation-based variants.<n>Hybrid-CASR reduces training time per window by about 17 percent compared to the baseline, whereas cumulative training delivers only a minor F1 increase (0.661) at a 15.9-fold computational cost.
• Score: 43.24339861841546
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work applies Large Language Models (LLMs) to source-code vulnerability detection, but most evaluations still rely on random train-test splits that ignore time and overestimate real-world performance. In practice, detectors are deployed on evolving code bases and must recognise future vulnerabilities under temporal distribution shift. This paper investigates continual fine-tuning of a decoder-style language model (microsoft/phi-2 with LoRA) on a CVE-linked dataset spanning 2018-2024, organised into bi-monthly windows. We evaluate eight continual learning strategies, including window-only and cumulative training, replay-based baselines and regularisation-based variants. We propose Hybrid Class-Aware Selective Replay (Hybrid-CASR), a confidence-aware replay method for binary vulnerability classification that prioritises uncertain samples while maintaining a balanced ratio of VULNERABLE and FIXED functions in the replay buffer. On bi-monthly forward evaluation Hybrid-CASR achieves a Macro-F1 of 0.667, improving on the window-only baseline (0.651) by 0.016 with statistically significant gains ($p = 0.026$) and stronger backward retention (IBR@1 of 0.741). Hybrid-CASR also reduces training time per window by about 17 percent compared to the baseline, whereas cumulative training delivers only a minor F1 increase (0.661) at a 15.9-fold computational cost. Overall, the results show that selective replay with class balancing offers a practical accuracy-efficiency trade-off for LLM-based temporal vulnerability detection under continuous temporal drift.

Related papers

• CVeDRL: An Efficient Code Verifier via Difficulty-aware Reinforcement Learning [57.24524263804788]
  Code verifiers play a critical role in post-verification for LLM-based code generation.<n>Existing supervised fine-tuning methods suffer from data scarcity, high failure rates, and poor inference efficiency.<n>We show that naive RL with only functionality rewards fails to generate effective unit tests for difficult branches and samples.
  arXiv  Detail & Related papers  (2026-01-30T10:33:29Z)
• Hybrid Ensemble Method for Detecting Cyber-Attacks in Water Distribution Systems Using the BATADAL Dataset [1.4975436239088316]
  We consider a hybrid ensemble learning model that will enhance the detection ability of cyber-attacks in Water Distribution Systems.<n>The proposed framework establishes a robust and scalable solution for cyber-attack detection in time-dependent industrial systems.
  arXiv  Detail & Related papers  (2025-12-16T14:07:22Z)
• Human-in-the-loop Online Rejection Sampling for Robotic Manipulation [55.99788088622936]
  Hi-ORS stabilizes value estimation by filtering out negatively rewarded samples during online fine-tuning.<n>Hi-ORS fine-tunes a pi-base policy to master contact-rich manipulation in just 1.5 hours of real-world training.
  arXiv  Detail & Related papers  (2025-10-30T11:53:08Z)
• Randomized Neural Network with Adaptive Forward Regularization for Online Task-free Class Incremental Learning [16.323995111105884]
  We propose a neural network (NN) with forward regularization (-F) to resist forgetting and enhance learning performance.<n>We derive the algorithm of the ensemble deep random vector functional link network (edRVFL) with adjustable forward regularization (-kF)<n>edRVFL-kF generates one-pass closed-form incremental updates and variable learning rates, effectively avoiding past replay and catastrophic forgetting.<n>We improve it to the plug-and-play edRVFL-kF-Bayes, enabling all hard ks in multiple sub-learners to be self-adaptively determined.
  arXiv  Detail & Related papers  (2025-10-24T11:50:13Z)
• Pentest-R1: Towards Autonomous Penetration Testing Reasoning Optimized via Two-Stage Reinforcement Learning [12.155067662797281]
  Pentest-R1 is a framework designed to optimize reasoning capabilities for penetration testing tasks.<n>It learns directly from environmental feedback to develop robust error self-correction and adaptive strategies.<n>On AutoPenBench, Pentest-R1 achieves a 24.2% success rate, surpassing most state-of-the-art models.
  arXiv  Detail & Related papers  (2025-08-10T15:14:05Z)
• Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training [71.16258800411696]
  Reinforcement learning (RL) is a critical component of large language model (LLM) post-training.<n>Existing on-policy algorithms used for post-training are inherently incompatible with the use of experience replay buffers.<n>We propose efficiently obtaining this benefit of replay buffers via Trajectory Balance with Asynchrony (TBA)
  arXiv  Detail & Related papers  (2025-03-24T17:51:39Z)
• Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
  Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
  arXiv  Detail & Related papers  (2023-12-27T09:03:43Z)
• Unbiased and Efficient Self-Supervised Incremental Contrastive Learning [31.763904668737304]
  We propose a self-supervised Incremental Contrastive Learning (ICL) framework consisting of a novel Incremental InfoNCE (NCE-II) loss function. ICL achieves up to 16.7x training speedup and 16.8x faster convergence with competitive results.
  arXiv  Detail & Related papers  (2023-01-28T06:11:31Z)
• Semi-supervised Contrastive Learning with Similarity Co-calibration [72.38187308270135]
  We propose a novel training strategy, termed as Semi-supervised Contrastive Learning (SsCL) SsCL combines the well-known contrastive loss in self-supervised learning with the cross entropy loss in semi-supervised learning. We show that SsCL produces more discriminative representation and is beneficial to few shot learning.
  arXiv  Detail & Related papers  (2021-05-16T09:13:56Z)

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.