Deep neural networks (DNNs) have become a foundational component of modern computing systems with a wide range of applications, such as computer vision, edge intelligence, etc. For the sake of low latency and data privacy, DNN models are increasingly compiled into executables and deployed on local devices. However, that exposes the models to model theft, enabling adversaries to recover proprietary assets via reverse engineering techniques. While code obfuscation naturally emerges for protecting executables from reverse engineering, existing schemes are primarily designed for traditional programs, focusing on complex control flow structures and integer-based operations. They are fundamentally inappropriate for DNN binaries, which exhibit relatively simple code structures and heavily rely on floating-point computation. In this paper, we propose FLOB, an obfuscation framework that protects floating-point computations in DNN binaries using Mixed Boolean-Arithmetic (MBA) transformations. Our approach lifts floating-point values into a higher-precision binary expansion space and performs MBA transformations at the representation level. The computation is carried out entirely in the lifted space, and the final result is then projected back to the target precision, yielding outputs that are semantically equivalent under the original floating-point format, without introducing additional rounding error. The experimental results show that FLOB outperforms the state-of-the-art obfuscation schemes against existing reverse engineering analyzers and deobfuscators, while preserving computational correctness without introducing additional numerical error, allowing flexible trade-off between protection and performance. Specifically, FLOB reduces the operator recovery rate to 4.51% on average, achieving a 32.82 percentage-point reduction compared to existing methods.
Yirui QI, Xiaoming Zhang Beijing University of Aeronautics and Astronautics, Ruilin Zeng, Mengyao Liu, Ziyi Zhou, Dezhuang Miao, Bingyu Yan Beijing University of Aeronautics and Astronautics, Zhenyu Guan Beijing University of Aeronautics and Astronautics
Current LLM role-playing systems model persona as a monolithic, static attribute, conflating identity consistency with emotional rigidity. This leads to either robotic repetition or catastrophic persona drift under sustained interaction. We introduce Dynamic Persona Coherence, a framework that decouples Identity-Layer Stability (time-invariant traits) from Adaptive-Layer Appropriateness (history-dependent psychological evolution). We operationalize this through the L/M/S Psychological State Model, which represents persona dynamics across long-term identity, mid-term meaning/stress accumulation, and short-term affect. On top of this state representation, a closed-loop alignment system comprising an automated evaluator (Persona Consistency Critic, PCC), a selective repository (Persona Case Repository, PCR), and a trajectory-adjusting corrector (Persona Drift Suppressor, PDS) enables autonomous coherence repair. Experiments on GPT-4o, Claude-3.5-Sonnet, and DeepSeek-V3.2 demonstrate consistent improvements (+16–84% PCC gains).
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Bingyu Yan Beijing University of Aeronautics and Astronautics, Xiaoming Zhang Beijing University of Aeronautics and Astronautics, JinYu Hou, Chaozhuo Li Beijing University of Aeronautics and Astronautics, Ziyi Zhou, Yiming Hei, Litian Zhang Beijing University of Post and Telecommunications
While Large Language Model-based Multi-Agent Systems (LLM-MAS) demonstrate remarkable capabilities in solving complex tasks by orchestrating specialized agents and external tools, the implicit trust in tool outputs creates a critical attack surface. Existing tool attacks are limited by domain specificity or fixed and static templates. To address these challenges, we propose Evo-Attacker, which formulates the tool attack as a self-evolving, memory-augmented reinforcement learning process. Evo-Attacker constructs a dynamic attack memory and employs deliberative reasoning to retrieve adversarial patterns and strategize modifying interventions at critical moments. Furthermore, we introduce Attack-Flow GRPO to optimize intermediate reasoning steps via terminal outcomes, addressing the long-horizon credit assignment challenge. Comprehensive experiments demonstrate that Evo-Attacker consistently outperforms baselines, highlighting its generalization and evolutionary capabilities and the urgent need for defensive tool safeguards.
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cs.CL · cs.MA
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表