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新论文 · v1arXiv:2611.10723

CyberBinGym: Can AI Agents Turn Binary Patches into Real Exploits?

Lambang Akbar Wijayadi, Zhaoqi Xiao, Zhun Wang, Hongwei Li, Wenbo Guo, Heng Yin, Zhenkai Liang, Dawn Song

原文摘要

Security fixes usually reach users as compiled updates, but deployment delays leave unpatched systems exposed. During this window, comparing patched and unpatched binaries can reveal how to exploit a vulnerability, turning a defensive update into actionable information for attackers. Existing benchmarks do not measure this capability: they expose source code, rely on human-crafted challenges, or evaluate binary comprehension without requiring exploitation. We introduce CyberBinGym, a benchmark containing 100 tasks derived from real vulnerabilities in 53 widely used projects, spanning six vulnerability families and three levels of analysis complexity. Agents receive stripped vulnerable and patched binaries without source code or vulnerability information, and must combine static and dynamic analysis to recover and exploit the vulnerability. Evaluation separately measures vulnerability recovery through differential crash testing and end-to-end exploitation through flag retrieval, with trajectory audits to reject reward hacking. The strongest evaluated agent recovers the target vulnerability in 88% of tasks and exploits it in 20%. Providing the patched binary raises its exploit rate from 10% to 25% on a matched 20-task subset; with standard binary hardening enabled, it still exploits two of its 20 previously successful tasks. These results demonstrate that current agents can turn released binary patches into working exploits in a controlled setting, although exploitation remains unreliable. These capabilities lower the barriers to targeting unpatched software and compress defenders' effective response window, increasing the urgency of timely patch deployment.

cs.CR · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09256

Zero-Query Transferable Pretend Benign Attacks with Amortized Online Generation for Cooperative Perception

Hongwei Lin, Xun Huang, Chenglu Wen, Cheng Wang

原文摘要

Cooperative perception enables an ego agent to acquire information from collaborators beyond its own sensors' reach. Yet, the most beneficial messages are often the hardest to verify from local observations. Pretend Benign (PB) exploits this asymmetry by targeting regions with weak local verifiability and applying multi-objective perturbations, making harmful feature changes appear to be benign assistance. However, PB is victim-dependent: selecting regions and optimizing perturbations require the victim model's architecture, parameters, and gradients, limiting transfer to unseen victims. A transferable attack must capture properties shared across cooperative detection models rather than those specific to one network. We address this by building a victim-agnostic attack from a heterogeneous ensemble of surrogate models and encoding its capabilities in a reusable dictionary to reduce per-message generation cost. Specifically, Transferable Pretend Benign (TrPB) transfers PB's region selection criterion and multi-objective perturbation strategy across heterogeneous surrogates to construct an offline attack. We further propose RT-TrPB, which encodes transferable regions and perturbation patterns into a region predictor and reusable perturbation dictionary, amortizing per-message attack generation. Experiments on widely used cooperative perception benchmarks show that RT-TrPB bypasses the consensus-based defenses ROBOSAC and FLD with success rates approaching 100% and generates each perturbation in under 56 ms. To counter PB-style attacks masquerading as benign collaboration, we also propose STTrust, which verifies received messages using spatial and temporal consistency and incorporates identity-level trust. STTrust detects over 90% of PB-style attacks and restores perception performance to near the attack-free level.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03154

PaddleMaterials: An End-to-End Deep Learning Toolkit for Materials Science

Xuwei Liu, Zhimin Zhang, Xiaoguang Hu, Zhongwei Liu, Yuanhui Sun, Jun Huo, Hua Wu, Haifeng Wang

原文摘要

Machine learning supports materials design, atomistic simulation and experimental characterization, yet models and workflows remain fragmented. We present PaddleMaterials, an open-source framework integrating heterogeneous materials-learning tasks while preserving task-specific representations and scientific semantics. It unifies data processing, training, evaluation, pretrained-model management and workflows across six task classes: property prediction, structure generation, machine-learning interatomic potentials, electronic-structure fields, spectrum elucidation and microscopy image processing. Task interfaces record physical quantities, units, aggregation conventions and references, making cross-task composition verifiable. A shared compiler backend enables switching from dynamic-graph to compiled execution through one setting, using graph optimization, operator fusion and hardware-targeted code generation without changing model definitions. Across 65 model instances in 11 groups, compiled execution accelerated every instance in eight groups, with group median speedups of 11%-224% and group maximum speedups of 11%-336%. Case studies on LiMnO2 and toluene link structure generation or spectrum elucidation, interatomic-potential relaxation, property prediction and electron-density prediction, using same-geometry density functional theory (DFT) calculations as references. For LiMnO2, predicted formation energies for generated and relaxed structures differed from same-geometry PBE+U references by at most 7 meV per atom, while the computational cost of model steps was less than one-twentieth of a DFT single-point calculation. PaddleMaterials extends unified materials workflows beyond conventional property, potential and structure-generation tasks while supporting verifiable cross-task operations and compiled execution.

cs.LG · cond-mat.mtrl-sci · cs.CE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39572

RV2GC: Reconstructing the Garbling Boundary in a RISC-V Garbled Processor

Zixiao Feng, Xiaolin Li, Chenglu Jin, Wei Yan, Yong Zhang, Hongwei Liu, Yong Liu, Qinfen Hao

原文摘要

Programmable garbled-circuit execution must balance the benefits of cross-program reuse against the cost of repeated work during secure execution. A complete garbled processor provides a fixed circuit for many programs, but its evaluator repeatedly visits processor-wide structures even when only a small part performs the current protected computation. We present RV2GC, which reconstructs the garbling boundary around a fixed library of RISC-V instruction circuits. Public scheduling and operand routing operate outside the circuit topologies; persistent label-encoded state connects independently invoked garbled instruction circuits and instruction-specific garbling-free paths through a common interface. A Conditional-execution Stack and protected commits support secret-dependent branches, bounded loops, and secret-indexed memory access under public scheduling, keeping instruction and memory-address sequences independent of private inputs. Across nine benchmarks, measured ARM2GC-to-RV2GC runtime ratios range from 12.81 to 4,043. In terms of circuit size, the largest listed instruction circuit has 3,386 gates, corresponding to 2.044% and 0.393% of the 256-byte and 4,096-byte Amber processors, respectively. Additional comparisons quantify schedule storage, communication, and the cost of secret-dependent execution.

cs.CR · cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34937

Subcodes with Prescribed Hull Dimensions in Linear Codes:Enumeration, Containment, and Asymptotic Distributions

Linhan Xiong, Hongwei Liu, Liren Lin

原文摘要

Mass formulas for prescribed hull dimensions have mainly been studied for linear codes viewed as subspaces of the whole space. We study internal enumeration for an arbitrary fixed parent code. Let $C$ be an $[n,k]_q$ linear code with an $\ell$-dimensional hull, and let $s$ be the subcode dimension. We determine the number of subcodes with prescribed dimension, hull dimension, and type over finite fields of both even and odd characteristic. We decompose the parent code into an LCD complement and its hull, and use projection to reduce enumeration to the corresponding problem inside the LCD complement. This yields explicit formulas refined by complete type and shows that the counts depend only on the relevant parameters, not on the particular parent code or its length. The known whole-space mass formulas and the enumeration of LCD subcodes of LCD codes arise as special cases. Motivated by code extension, we also study the dual containment problem: for a fixed code, we enumerate larger codes containing it with prescribed hull dimension and type. By duality, this problem is converted into the subcode enumeration problem already solved above, leading to corresponding conditional mass formulas. Finally, we investigate the asymptotic distributions arising from the exact enumeration formulas. When both the subcode dimension $s$ and $k-\ell-s$ tend to infinity, the hull distribution is governed by the LCD complement, and the limiting proportions are explicit constants determined by the field, the prescribed hull dimension, and the relevant type. When $s$ approaches or exceeds the dimension $k-\ell$ of the LCD complement, we determine the asymptotic proportion of subcodes attaining the minimum possible hull dimension and establish the associated concentration behavior. Analogous asymptotic results are obtained for the containment problem.

cs.IT

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arXiv ↗PDF ↗