arXiv AI

Full-range Binary Classifier Calibration for Stable Model Updates in Production

arXiv:2607. 05481v1 Announce Type: cross Abstract: Detection models running in adversarial environments face a malicious distribution that drifts rapidly while the benign distribution stays comparatively stable, so teams retrain and redeploy constantly to stay ahead of new threats.

arXiv AI

Jun 19

Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software

arXiv:2606. 20502v1 Announce Type: cross Abstract: Whether LLMs scoring well on vulnerability benchmarks genuinely reason about security or merely pattern-match on contaminated data remains unresolved.

By Arastoo Zibaeirad, Marco Vieira

llms fine-tuning benchmarks

arXiv AI

3d ago

Regime-Conditional Verification: Correctness Estimation for Adapting and Monitoring Safety Classifiers

arXiv:2608. 14089v1 Announce Type: new Abstract: Safety classifiers deployed with large language models often fail for two reasons: their decisions reflect the policy learned during training rather than the deployer's desired policy, and their performance degrades as deployment traffic evolves.

By Thiago Sandoval, Ufuk Topcu

llms fine-tuning benchmarks safety

arXiv Machine Learning

Aug 12

ProbGuard: Calibrated Safety Risk Estimation from LLM Output Distributions

arXiv:2608. 10621v1 Announce Type: new Abstract: Recent research on Large Language Model (LLM) safety has widely adopted guardrails to identify unsafe LLM outputs.

By Xinzhe Huang, Biwu Yao, Kedong Xiu, Mengnan Zhao, Di Wang, Puning Zhao, Tianhang Zheng

llms safety

arXiv AI

Jun 3

High-Precision APT Malware Attribution with Out-of-Scope Resilience

arXiv:2606. 03523v1 Announce Type: cross Abstract: Early attribution of Advanced Persistent Threat (APT) activity can help defenders prioritise investigation, select countermeasures, and reduce the impact of an intrusion.

By Peter Williams, Adam Sobey, Erisa Karafili

arXiv Machine Learning

Jul 21

When Drift Detectors cry Wolf: False Alarm Rates in continuous ML Monitoring

arXiv:2607. 17336v1 Announce Type: new Abstract: Drift detection is a core component of production machine learning monitoring systems, where detectors are used to compare incoming data with a reference distribution and trigger alerts when changes occur.

By Raj Shekhar Singh

safety

arXiv Machine Learning

Jul 1

An Empirical Study of Security Calibration in Large Language Models for Code

arXiv:2606. 31159v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated code is insecure?

By Mohammed Latif Siddiq, Md. Nafiu Rahman, Joanna C. S. Santos

llms benchmarks

arXiv Machine Learning

Jun 18

Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution

arXiv:2606. 19023v1 Announce Type: cross Abstract: The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces.

By Gabriele Digregorio, Marco Di Gennaro, Francesco Pastore, Stefano Zanero, Stefano Longari, Michele Carminati

benchmarks

arXiv AI

Jun 6

GuardNet: Ensemble Strategies of Shallow Neural Networks for Robust Prompt Injection and Jailbreak Detection

arXiv:2606. 05566v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but they remain vulnerable to Prompt Injection (PI) and Jailbreak (JB) attacks.

By Paulo Ricardo Ferreira Neves, Edson Rodrigues da Cruz Filho, Paulo Henrique Eleuterio Falsetti, Jo\~ao Vitor Pavan, Ian Degaspari, Henrique Vieira Laturrague, Patrick Vieira Laturrague, Guilherme Nielsen Dias, Marccello Wilson Perez Berto, Gustavo Voltani Von Atzingen

llms nlp benchmarks safety

arXiv Machine Learning

Jun 11

Online Shift Detection and Conformal Adaptation for Deployed Safety Classifiers

arXiv:2606. 11949v1 Announce Type: new Abstract: We present an online monitoring system for distributional shift in deployed safety classifiers, using calibrated sequential statistics to detect when a classifier has moved out of distribution.

By Jun Wen Leong

llms rag safety

arXiv Machine Learning

Jul 20

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications

arXiv:2508. 00042v2 Announce Type: replace-cross Abstract: Machine learning models deployed in non-stationary environments degrade silently, since as the input distribution drifts their accuracy decays without an error signal and without labels to reveal it.

By Athanasios Tziouvaras, Carolina Fortuna, George Floros, Kostas Kolomvatsos, Panagiotis Sarigiannidis, Marko Grobelnik, Bla\v{z} Bertalani\v{c}

arXiv AI

Jun 8

Zero-Shot Embedding Drift Detection: A Lightweight Defense Against Prompt Injections in LLMs

arXiv:2601. 12359v1 Announce Type: cross Abstract: Prompt injection attacks have become an increasing vulnerability for LLM applications, where adversarial prompts exploit indirect input channels such as emails or user-generated content to circumvent alignment safeguards and induce harmful or unintended outputs.

By Anirudh Sekar, Mrinal Agarwal, Rachel Sharma, Akitsugu Tanaka, Jasmine Zhang, Arjun Damerla, Kevin Zhu

llms rag benchmarks safety

arXiv AI

Jun 16

InstantForget: Update-Free Backdoor Unlearning with Inference-Time Feature Reset

arXiv:2606. 15730v1 Announce Type: cross Abstract: Backdoor unlearning aims to remove a malicious trigger behavior from a deployed model while preserving clean utility.

By Zhenyu Yu

multimodal

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