arXiv AI September 23, 2026 By Fernando Outeda, Gustavo Betarte, Juan Diego Campo, Fiorella Cravero

Decoding Guardrails: XAI-Guided Perturbation Analysis of Prompt Injection Detection

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Machine Learning

Aug 7

Robust Context-Aware Detection of Malicious Instructions in Text

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.

By Buzhao Liu, Xinhang Ma, Yevgeniy Vorobeychik

llms rag agents computer-vision benchmarks safety

arXiv AI

Sep 10

SAFEGuard: Detect Optimization-Based Jailbreak Attacks Through Harmful Semantic Analysis and Fluency Measurement

arXiv:2609.05850v1 Announce Type: cross Abstract: Despite the significant efforts devoted to aligning large language models (LLMs) with human values and ensuring safe deployment, recent work has reve...

By Quoc Viet Vo, Trung Le, Damith C. Ranasinghe, Ehsan Abbasnejad

llms benchmarks safety

arXiv Machine Learning

Jun 9

What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks

arXiv:2606. 09700v1 Announce Type: cross Abstract: Large language model (LLM)-powered content moderation systems have become a critical defense against harmful online content.

By Qin Yang, Lu Malloy, Joshua Lee, Xiaohan Chang, Meisam Mohammady, Doowon Kim, Yuan Hong

llms benchmarks safety

Hugging Face Trending Papers

Jul 22

Defense Against LLM Backdoors using Critical Neuron Isolation Pruning

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.

llms fine-tuning efficiency multimodal benchmarks

arXiv AI

Aug 24

Truth Lies Deep: Countering Semantic Camouflage via Latent Intent Verification

The paper identifies a vulnerability in large language models where harmful intent can be hidden within benign narratives, a phenomenon termed Semantic Camouflage. By examining latent activation patterns across several small language model families, the authors discover an "Intent Horizon"—a layer depth where harmful intent representations collapse. They propose Latent Intent Verification (LIV), a lightweight probing defense that detects harmful intent in early layers and outperforms existing guardrails on the PKU-SafeRLHF dataset.

By Md. Hasib Ur Rahman

llms safety

Hugging Face Trending Papers

Jul 29

ToxScreen: Detecting Whether an LLM Has Been Poisoned

As large language models (LLMs) are deployed in high-stakes domains, adversaries may poison training data to implant backdoors: hidden triggers that covertly manipulate model behavior at inference time. We ask whether a defender can recover such a trigger under realistic affordances, namely white-box access to the weights and knowledge of the behavior of concern, but no training data, no trusted reference model, no knowledge of the trigger, and no certainty that the model is poisoned.

llms benchmarks