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新论文 · v1arXiv:2611.13684

BypassGym: A Benchmark for Evaluating AI Agents' Endpoint Defense Evasion Capabilities

Weifeng Yuan, Zifan Chen, Yucheng Zhang, Feng Dong, Peng Gao, Haoyu Wang

原文摘要

Artificial intelligence agents increasingly automate cybersecurity tasks, including offensive operations. Frequent attacks across the web make endpoint detection and response (EDR) a critical defense, but whether AI agents can evade it remains unclear. Benchmarks evaluate the offensive capabilities of large language model (LLM) agents in areas such as penetration testing and vulnerability discovery. One important capability that these benchmarks do not address is defense evasion, specifically whether an agent can develop a program that executes a payload without being blocked or terminated by a deployed EDR. We introduce BypassGym, to our knowledge the first benchmark to systematically evaluate this capability on real endpoints. Given command-and-control (C2) shellcode, an agent must generate and compile a loader that executes the payload without being blocked or terminated. A supplementary evasion knowledge skill provides domain knowledge and supports comparisons with and without this input. BypassGym is built on Proxmox VE(PVE), so the evaluation environment runs real Windows systems and EDR products inside virtual machines and returns structured detection feedback to the agent. We evaluate 5 lightweight model backends against 6 EDR products and 3 payloads, and find that even these simple models generate successful submissions, with DeepSeek-V4.1 Flash reaching 17/18 successful runs and Bitdefender allowing only 5 of 15 runs to pass. These results provide a systematic measurement of current AI agents' endpoint defense evasion capabilities and identify areas for defenders to investigate.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.08322

Fundus-R1: Training a Fundus-Reading MLLM on Public Data

Yuchuan Deng, Qijie Wei, Kaiheng Qian, Jiazhen Liu, Bangxiang Lan, Qingwei Shen, Jianfeng Dong, Weihong Yu 等 9 位作者

原文摘要

Fundus imaging such as CFP, OCT and SLO is crucial for the early detection of retinal anomalies and diseases. Fundus image understanding, due to its knowledge-intensive nature, poses a challenging vision-language task. An emerging approach to addressing the task is to post-train a generic multimodal large language model (MLLM), either by supervised finetuning (SFT) or by reinforcement learning with verifiable rewards (RLVR), on a considerable amount of in-house samples paired with high-quality clinical reports. However, these valuable samples are not publicly accessible, which not only hinders reproducibility but also practically limits research to few players. In this paper, we make a novel attempt to train a reasoning-enhanced fundus-reading MLLM, termed Fundus-R1, using exclusively public datasets, wherein over 94% of the data are annotated with image-level labels only. Our technical contributions are two-fold. First, we propose a RAG-based method for composing image-specific, knowledge-aware reasoning traces. Such auto-generated traces link visual findings identified by a generic MLLM to the image labels in terms of ophthalmic knowledge. Second, we enhance RLVR with a process reward that encourages self-consistency of the generated reasoning trace in each rollout. Extensive experiments on three fundus-reading benchmarks, i.e., FunBench, Omni-Fundus and GMAI-Fundus, show that Fundus-R1 clearly outperforms multiple baselines, including its generic counterpart (Qwen2.5-VL) and a stronger edition post-trained without using the generated traces. This work paves the way for training powerful fundus-reading MLLMs with publicly available data.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04529

Learning to Falsify: Hierarchical Multiple Instance Learning for High Resolution AI-Generated Image Detection

Haoran Chen, Daiqi Gao, Xinyuan Li, Yongwei Wang, Lei Zhu, Rui Cai, Jianfeng Dong

原文摘要

Recent advances in generative models have made synthetic images increasingly photorealistic, posing growing challenges to reliable and generalizable detection of AI-generated images. Existing methods typically rely on holistic image-level classification, which may overlook local artifacts, especially in high-resolution images. To address this limitation, we formulate this task as a falsification-driven multiple instance learning problem, where a single fake patch can falsify the whole image, while a real image requires all patches to be authentic. Based on this formulation, we propose \textit{Learning to Falsify (LTF)}, a hierarchical multiple instance learning framework that models an image as multiple bags of instances across different spatial levels and aggregates their predictions under the falsification-inspired principle. We further introduce Cross-Shuffle during training to encourage the model to learn local artifact evidence rather than image-level shortcuts. Furthermore, we propose the \textit{Falsification-driven Test-time Aggregation Strategy (FATAS)}, a plug-and-play module that extends the same falsification-inspired principle to existing detectors by aggregating patch-level predictions. Extensive experiments demonstrate that LTF achieves strong generalization and robustness across multiple benchmarks, while FATAS substantially improves representative detectors in high-resolution scenarios without retraining. Code is available at https://github.com/HuiGuanLab/LTF.git

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36509

ARISE: Authentic Reference-Informed Stability Enhancement for Continual Multimodal Forgery Detection

Yuchen Zhang, Yaxiong Wang, Yujiao Wu, Lianwei Wu, Qi Zhang, Li Zhu, Chaofeng Dong, Le Wu 等 9 位作者

原文摘要

Multimodal forgery detection is commonly studied under a static assumption: training attacks are expected to approximate those encountered after deployment. This assumption becomes increasingly fragile as generators, editing systems, and multimodal large language models continually introduce new forgery mechanisms and forensic evidence. We formulate this problem as attack-incremental continual multimodal forgery detection, where the binary real/fake interface remains fixed while emerging attacks must be learned without forgetting previous ones. We introduce Fable-Bench, which organizes attack evolution through complementary joint-modality, text-side, and image-side protocols. We further propose ARISE (Authentic Reference-Informed Stability Enhancement), motivated by the open-ended forged class and the comparatively reusable geometry of authentic content. ARISE fits a real-only Gaussian mixture model to projected authentic replay representations as a compact historical reference. Meanwhile, the proposed Gaussian-Responsibility-Aware Stability Preservation (GRASP) derives a sample-adaptive anisotropic metric that strongly constrains historically stable representation directions while retaining plasticity elsewhere. Our Vulnerability-Aware Replay (VAR) further estimates stage-wise vulnerability from authentic-density degradation, forged-to-authentic boundary confusion, and authentic-mode occupancy drift, then assigns bounded replay emphasis to fragile stages. All adaptive signals are obtained from training-time replay statistics, while inference uses only the original multimodal backbone and binary classification head. Experiments across three attack-evolution protocols and multiple detector architectures demonstrate improved historical retention and final average performance over representative continual-learning baselines.

cs.CV

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arXiv ↗PDF ↗