arXiv:2503.11841v2 Announce Type: replace-cross Abstract: Machine Learning (ML) malware detectors rely heavily on crowd-sourced AntiVirus (AV) labels, with platforms like VirusTotal serving as truste...
Large language models (LLMs) are vulnerable to backdoor attacks, where hidden triggers induce malicious outputs. Existing defenses generally fall into inference-time detection or training-time mitigation, but face two key limitations.
arXiv:2608.00745v2 Announce Type: replace Abstract: Modern endpoint detection systems face a fundamental tension: signature-based approaches are trivially evaded by polymorphic or adaptive threats, w...
arXiv:2607. 17105v1 Announce Type: cross Abstract: It is crucial to safeguard computer networks from evolving network security threats and unknown cyberattacks.
arXiv:2608. 05430v1 Announce Type: cross Abstract: The remarkable instruction-following ability of modern LLMs has enabled their practical use as the minds of agents that can autonomously complete increasingly complex tasks.
The paper demonstrates that a single poisoned data point can successfully create a backdoor in linear models and ReLU neural networks without needing detailed knowledge of the training data. It establishes provable conditions under which this one‑poison attack works with high probability, achieving zero backdooring error while leaving the model’s normal performance largely unaffected. The attack relies only on coarse geometric bounds of the input space and training parameters.
arXiv:2607. 19894v1 Announce Type: cross Abstract: Large language models (LLMs) are vulnerable to backdoor attacks, where hidden triggers induce malicious outputs.
The paper evaluates how graph neural networks (GNNs) built on control flow graphs (CFGs) perform when trained on one time period and tested on a later one, using a strict temporal split. Twelve GNN variants and a flat-feature baseline were trained on 459 CFGs from 2024‑2025 and evaluated on 223 CFGs from 2026, revealing that the choice of message‑passing operator strongly affects robustness to distribution shift. Attribution stability varied by architecture, and the best-performing operator on the later corpus was also the hardest to explain, leading the authors to design a new architecture that matches its performance without search.
arXiv:2507. 18313v2 Announce Type: replace Abstract: Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated.
arXiv:2608. 02671v1 Announce Type: cross Abstract: Malware detection using Hardware Performance Counters (HPC) has emerged as a promising solution to improve the security of computing systems as a complement to antivirus software.
arXiv:2509. 14335v2 Announce Type: replace-cross Abstract: Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them with code evidence.
The paper introduces a threat model for malicious intent injection in AI‑native 6G networks and examines four injection strategies: stealth‑mode, random distribution, increasing frequency, and decreasing frequency. It proposes a dual‑path detection framework combining a CNN with TF‑IDF features for supervised detection and an AutoEncoder trained on benign data for one‑class detection. Evaluation shows the framework achieves higher accuracy (0.97) and F1‑score (0.98) than the state‑of‑the‑art baseline.