Session 5D: Side Channels 1

Authors, Creators & Presenters: Lukas Maar (Graz University of Technology), Jonas Juffinger (Graz University of Technology), Thomas Steinbauer (Graz University of Technology), Daniel Gruss (Graz University of Technology), Stefan Mangard (Graz University of Technology)

PAPER
KernelSnitch: Side Channel-Attacks On Kernel Data Structures

The sharing of hardware elements, such as caches, is known to introduce microarchitectural side-channel leakage. One approach to eliminate this leakage is to not share hardware elements across security domains. However, even under the assumption of leakage-free hardware, it is unclear whether other critical system components, like the operating system, introduce software-caused side-channel leakage. In this paper, we present a novel generic software side-channel attack, KernelSnitch, targeting kernel data structures such as hash tables and trees. These structures are commonly used to store both kernel and user information, e.g., metadata for userspace locks. KernelSnitch exploits that these data structures are variable in size, ranging from an empty state to a theoretically arbitrary amount of elements. Accessing these structures requires a variable amount of time depending on the number of elements, i.e., the occupancy level. This variance constitutes a timing side channel, observable from user space by an unprivileged, isolated attacker. While the timing differences are very low compared to the syscall runtime, we demonstrate and evaluate methods to amplify these timing differences reliably. In three case studies, we show that KernelSnitch allows unprivileged and isolated attackers to leak sensitive information from the kernel and activities in other processes. First, we demonstrate covert channels with transmission rates up to 580 kbit/s. Second, we perform a kernel heap pointer leak in less than 65 s by exploiting the specific indexing that Linux is using in hash tables. Third, we demonstrate a website fingerprinting attack, achieving an F1 score of more than 89 %, showing that activity in other user programs can be observed using KernelSnitch. Finally, we discuss mitigations for our hardware-agnostic attacks.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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Session 5C: Federated Learning 1

Authors, Creators & Presenters: Duanyi Yao (Hong Kong University of Science and Technology), Songze Li (Southeast University), Xueluan Gong (Wuhan University), Sizai Hou (Hong Kong University of Science and Technology), Gaoning Pan (Hangzhou Dianzi University)

PAPER
URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning

Vertical Federated Learning (VFL) is a collaborative learning paradigm designed for scenarios where multiple clients share disjoint features of the same set of data samples. Albeit a wide range of applications, VFL is faced with privacy leakage from data reconstruction attacks. These attacks generally fall into two categories: honest-but-curious (HBC), where adversaries steal data while adhering to the protocol; and malicious attacks, where adversaries breach the training protocol for significant data leakage. While most research has focused on HBC scenarios, the exploration of malicious attacks remains limited. Launching effective malicious attacks in VFL presents unique challenges: 1) Firstly, given the distributed nature of clients' data features and models, each client rigorously guards its privacy and prohibits direct querying, complicating any attempts to steal data; 2) Existing malicious attacks alter the underlying VFL training task, and are hence easily detected by comparing the received gradients with the ones received in honest training. To overcome these challenges, we develop URVFL, a novel attack strategy that evades current detection mechanisms. The key idea is to integrate a discriminator with auxiliary classifier that takes a full advantage of the label information and generates malicious gradients to the victim clients: on one hand, label information helps to better characterize embeddings of samples from distinct classes, yielding an improved reconstruction performance; on the other hand, computing malicious gradients with label information better mimics the honest training, making the malicious gradients indistinguishable from the honest ones, and the attack much more stealthy. Our comprehensive experiments demonstrate that URVFL significantly outperforms existing attacks, and successfully circumvents SOTA detection methods for malicious attacks. Additional ablation studies and evaluations on defenses further underscore the robustness and effectiveness of URVFL

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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Session 5C: Federated Learning 1

Authors, Creators & Presenters: Dzung Pham (University of Massachusetts Amherst), Shreyas Kulkarni (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)

PAPER
RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation

Federated learning has emerged as a promising privacy-preserving solution for machine learning domains that rely on user interactions, particularly recommender systems and online learning to rank. While there has been substantial research on the privacy of traditional federated learning, little attention has been paid to the privacy properties of these interaction-based settings. In this work, we show that users face an elevated risk of having their private interactions reconstructed by the central server when the server can control the training features of the items that users interact with. We introduce RAIFLE, a novel optimization-based attack framework where the server actively manipulates the features of the items presented to users to increase the success rate of reconstruction. Our experiments with federated recommendation and online learning-to-rank scenarios demonstrate that RAIFLE is significantly more powerful than existing reconstruction attacks like gradient inversion, achieving high performance consistently in most settings. We discuss the pros and cons of several possible countermeasures to defend against RAIFLE in the context of interaction-based federated learning. Our code is open-sourced at https://github.com/dzungvpham/raifle
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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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Session 5C: Federated Learning 1

Authors, Creators & Presenters: Phillip Rieger (Technical University of Darmstadt), Alessandro Pegoraro (Technical University of Darmstadt), Kavita Kumari (Technical University of Darmstadt), Tigist Abera (Technical University of Darmstadt), Jonathan Knauer (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

PAPER
SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning

Split Learning (SL) is a distributed deep learning approach enabling multiple clients and a server to collaboratively train and infer on a shared deep neural network (DNN) without requiring clients to share their private local data. The DNN is partitioned in SL, with most layers residing on the server and a few initial layers and inputs on the client side. This configuration allows resource-constrained clients to participate in training and inference. However, the distributed architecture exposes SL to backdoor attacks, where malicious clients can manipulate local datasets to alter the DNN's behavior. Existing defenses from other distributed frameworks like Federated Learning are not applicable, and there is a lack of effective backdoor defenses specifically designed for SL. We present SafeSplit, the first defense against client-side backdoor attacks in Split Learning (SL). SafeSplit enables the server to detect and filter out malicious client behavior by employing circular backward analysis after a client's training is completed, iteratively reverting to a trained checkpoint where the model under examination is found to be benign. It uses a two-fold analysis to identify client-induced changes and detect poisoned models. First, a static analysis in the frequency domain measures the differences in the layer's parameters at the server. Second, a dynamic analysis introduces a novel rotational distance metric that assesses the orientation shifts of the server's layer parameters during training. Our comprehensive evaluation across various data distributions, client counts, and attack scenarios demonstrates the high efficacy of this dual analysis in mitigating backdoor attacks while preserving model utility.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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Session 5C: Federated Learning 1

Authors, Creators & Presenters: Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

PAPER
Passive Inference Attacks on Split Learning via Adversarial Regularization

Split Learning (SL) has emerged as a practical and efficient alternative to traditional federated learning. While previous attempts to attack SL have often relied on overly strong assumptions or targeted easily exploitable models, we seek to develop more capable attacks. We introduce SDAR, a novel attack framework against SL with an honest-but-curious server. SDAR leverages auxiliary data and adversarial regularization to learn a decodable simulator of the client's private model, which can effectively infer the client's private features under the vanilla SL, and both features and labels under the U-shaped SL. We perform extensive experiments in both configurations to validate the effectiveness of our proposed attacks. Notably, in challenging scenarios where existing passive attacks struggle to reconstruct the client's private data effectively, SDAR consistently achieves significantly superior attack performance, even comparable to active attacks. On CIFAR-10, at the deep split level of 7, SDAR achieves private feature reconstruction with less than 0.025 mean squared error in both the vanilla and the U-shaped SL, and attains a label inference accuracy of over 98% in the U-shaped setting, while existing attacks fail to produce non-trivial results.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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Session 4B: Audio Security

Authors, Creators & Presenters:

PAPER
VoiceRadar: Voice Deepfake Detection using Micro-Frequency And Compositional Analysis
Recent advancements in synthetic speech generation, including text-to-speech (TTS) and voice conversion (VC) models, allow the generation of convincing synthetic voices, often referred to as audio deepfakes. These deepfakes pose a growing threat as adversaries can use them to impersonate individuals, particularly prominent figures, on social media or bypass voice authentication systems, thus having a broad societal impact. The inability of state-of-the-art verification systems to detect voice deepfakes effectively is alarming. We propose a novel audio deepfake detection method, VoiceRadar, that augments machine learning with physical models to approximate frequency dynamics and oscillations in audio samples. This significantly enhances detection capabilities. VoiceRadar leverages two main physical models: (i) the Doppler effect to understand frequency changes in audio samples and (ii) drumhead vibrations to decompose complex audio signals into component frequencies. VoiceRadar identifies subtle variations, or micro-frequencies, in the audio signals by applying these models. These micro-frequencies are aggregated to compute the observed frequency, capturing the unique signature of the audio. This observed frequency is integrated into the machine learning algorithm's loss function, enabling the algorithm to recognize distinct patterns that differentiate human-produced audio from AI-generated audio. We constructed a new diverse dataset to comprehensively evaluate VoiceRadar, featuring samples from leading TTS and VC models. Our results demonstrate that VoiceRadar outperforms existing methods in accurately identifying AI-generated audio samples, showcasing its potential as a robust tool for audio deepfake detection.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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Session4A: IoT Security

Authors, Creators & Presenters: Eman Maali (Imperial College London), Omar Alrawi (Georgia Institute of Technology), Julie McCann (Imperial College London)

PAPER
Evaluating Machine Learning-Based IoT Device Identification Models for Security Applications

With the proliferation of IoT devices, network device identification is essential for effective network management and security. Many exhibit performance degradation despite the potential of machine learning-based IoT device identification solutions. Degradation arises from the assumption of static IoT environments that do not account for the diversity of real-world IoT networks, as devices operate in various modes and evolve over time. In this paper, we evaluate current IoT device identification solutions using curated datasets and representative features across different settings. We consider key factors that affect real-world device identification, including modes of operation, spatio-temporal variations, and traffic sampling, and organise them into a set of attributes by which we can evaluate current solutions. We then use machine learning explainability techniques to pinpoint the key causes of performance degradation. This evaluation uncovers empirical evidence of what continuously identifies devices, provides valuable insights, and practical recommendations for network operators to improve their IoT device identification in operational deployments

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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Session4A: IoT Security

Authors, Creators & Presenters: Haoqiang Wang, Yiwei Fang (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences; Indiana University Bloomington), Yichen Liu (Indiana University Bloomington), Ze Jin (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences; Indiana University Bloomington), Emma Delph (Indiana University Bloomington), Xiaojiang Du (Stevens Institute of Technology), Qixu Liu (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences), Luyi Xing (Indiana University Bloomington)

PAPER

Hidden and Lost Control: on Security Design Risks in IoT User-Facing Matter Controller

Matter is emerging as an IoT industry--unifying standard, aiming to enhance the interoperability among diverse smart home products, enabling them to work securely and seamlessly together. With many popular IoT vendors increasingly supporting Matter in consumer IoT products, we perform a systematic study to investigate how and whether vendors can integrate Matter securely into IoT systems and how well Matter as a standard supports vendors' secure integration. By analyzing Matter development model in the wild, we reveal a new kind of design flaw in user-facing Matter control capabilities and interfaces, called UMCCI flaws, which are exploitable vulnerabilities in the design space and seriously jeopardize necessary control and surveillance capabilities of Matter-enabled devices for IoT users. Therefore we built an automatic tool called UMCCI Checker, enhanced by the large-language model in UI analysis, which enables automatically detecting UMCCI flaws without relying on real IoT devices. Our tool assisted us with studying and performing proof-of-concept attacks on 11 real Matter devices of 8 popular vendors to confirm that the UMCCI flaws are practical and common. We reported UMCCI flaws to related vendors, which have been acknowledged by CSA, Apple, Tuya, Aqara, etc. To help CSA and vendors better understand and avoid security flaws in developing and integrating IoT standards like Matter, we identify two categories of root causes and propose immediate fix recommendations.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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Session4A: IoT Security

Authors, Creators & Presenters: Hangtian Liu (Information Engineering University), Lei Zheng (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Shuitao Gan (Laboratory for Advanced Computing and Intelligence Engineering), Chao Zhang (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Zicong Gao (Information Engineering University), Hongqi Zhang (Henan Key Laboratory of Information Security), Yishun Zeng (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Zhiyuan Jiang (National University of Defense Technology), Jiahai Yang (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University)

PAPER

EAGLEYE: Exposing Hidden Web Interfaces in IoT Devices via Routing Analysis [https://www.ndss-symposium.org/wp-con...](https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbEEzMmJxSkNwUUhDUkMteHZraTQ1blZ5Sk0zUXxBQ3Jtc0tuZldzQXZxQXJaOGt0VDU2RGNPdGVSbnMzcWxiTVZ1UmJsTzcyaUlCTFdvbmhoWnZRdWQ0UlJiUEs4ekR1UXNCNF9KQmp4UGxKOG5kMHdBdHBiaWh6ckxFaGphY0JVRDZDQ21jUWcyREx2Qy1XVTJqWQ&q=https%3A%2F%2Fwww.ndss-symposium.org%2Fwp-content%2Fuploads%2F2025-399-paper.pdf&v=qXDD2iiIeCg) Hidden web interfaces, i.e., undisclosed access channels in IoT devices, introduce great security risks and have resulted in severe attacks in recent years. However, the definition of such threats is vague, and few solutions are able to discover them. Due to their hidden nature, traditional bug detection solutions (e.g., taint analysis, fuzzing) are hard to detect them. In this paper, we present a novel solution EAGLEYE to automatically expose hidden web interfaces in IoT devices. By analyzing input requests to public interfaces, we first identify routing tokens within the requests, i.e., those values (e.g., actions or file names) that are referenced and used as index by the firmware code (routing mechanism) to find associated handler functions. Then, we utilize modern large language models to analyze the contexts of such routing tokens and deduce their common pattern, and then infer other candidate values (e.g., other actions or file names) of these tokens. Lastly, we perform a hidden-interface directed black-box fuzzing, which mutates the routing tokens in input requests with these candidate values as the high-quality dictionary. We have implemented a prototype of EAGLEYE and evaluated it on 13 different commercial IoT devices. EAGLEYE successfully found 79 hidden interfaces, 25X more than the state-of-the-art (SOTA) solution IoTScope. Among them, we further discovered 29 unknown vulnerabilities including backdoor, XSS (cross-site scripting), command injection, and information leakage, and have received 7 CVEs.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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Session4A: IoT Security

Authors, Creators & Presenters: Christopher Ellis (The Ohio State University), Yue Zhang (Drexel University), Mohit Kumar Jangid (The Ohio State University), Shixuan Zhao (The Ohio State University), Zhiqiang Lin (The Ohio State University)

PAPER

Deanonymizing Device Identities via Side-channel Attacks in Exclusive-use IoTs & Mitigation Wireless technologies like Bluetooth Low Energy (BLE) and Wi-Fi are essential to the Internet of Things (IoT), facilitating seamless device communication without physical connections. However, this convenience comes at a cost--exposed data exchanges that are susceptible to observation by attackers, leading to serious security and privacy threats such as device tracking. Although protocol designers have traditionally relied on strategies like address and identity randomization as a countermeasure, our research reveals that these attacks remain a significant threat due to a historically overlooked, fundamental flaw in exclusive-use wireless communication. We define exclusive-use as a scenario where devices are designed to provide functionality solely to an associated or paired device. The unique communication patterns inherent in these relationships create an observable boolean side-channel that attackers can exploit to discover whether two devices "trust" each other. This information leak allows for the deanonymization of devices, enabling tracking even in the presence of modern countermeasures. We introduce our tracking attacks as IDBleed and demonstrate that BLE and Wi-Fi protocols that support confidentiality, integrity, and authentication remain vulnerable to deanonymization due to this fundamental flaw in exclusive-use communication patterns. Finally, we propose and quantitatively evaluate a generalized, privacy-preserving mitigation we call Anonymization Layer to find a negligible 2% approximate overhead in performance and power consumption on tested smartphones and PCs.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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SESSION
Session 3D: AI Safety

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Authors, Creators & Presenters: Yan Pang (University of Virginia), Aiping Xiong (Penn State University), Yang Zhang (CISPA Helmholtz Center for Information Security), Tianhao Wang (University of Virginia)

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PAPER
Towards Understanding Unsafe Video Generation
Video generation models (VGMs) have demonstrated the capability to synthesize high-quality output. It is important to understand their potential to produce unsafe content, such as violent or terrifying videos. In this work, we provide a comprehensive understanding of unsafe video generation.

First, to confirm the possibility that these models could indeed generate unsafe videos, we choose unsafe content generation prompts collected from 4chan and Lexica, and three open-source SOTA VGMs to generate unsafe videos. After filtering out duplicates and poorly generated content, we created an initial set of 2112 unsafe videos from an original pool of 5607 videos. Through clustering and thematic coding analysis of these generated videos, we identify 5 unsafe video categories: Distorted/Weird, Terrifying, Pornographic, Violent/Bloody, and Political. With IRB approval, we then recruit online participants to help label the generated videos. Based on the annotations submitted by 403 participants, we identified 937 unsafe videos from the initial video set. With the labeled information and the corresponding prompts, we created the first dataset of unsafe videos generated by VGMs. We then study possible defense mechanisms to prevent the generation of unsafe videos. Existing defense methods in image generation focus on filtering either input prompt or output results. We propose a new approach called sysname, which works within the model's internal sampling process. sysname can achieve 0.90 defense accuracy while reducing time and computing resources by 10 times when sampling a large number of unsafe prompts. Our experiment includes three open-source SOTA video diffusion models, each achieving accuracy rates of 0.99, 0.92, and 0.91, respectively. Additionally, our method was tested with adversarial prompts and on image-to-video diffusion models, and achieved nearly 1.0 accuracy on both settings. Our method also shows its interoperability by improving the performance of other defenses when combined with them.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

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Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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SESSION
Session 3D: AI Safety

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Authors, Creators & Presenters: Haotian Zhu (Nanjing University of Science and Technology), Shuchao Pang (Nanjing University of Science and Technology), Zhigang Lu (Western Sydney University), Yongbin Zhou (Nanjing University of Science and Technology), Minhui Xue (CSIRO's Data61)

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PAPER
GAP-Diff: Protecting JPEG-Compressed Images From Diffusion-Based Facial Customization
Text-to-image diffusion model's fine-tuning technology allows people to easily generate a large number of customized photos using limited identity images. Although this technology is easy to use, its misuse could lead to violations of personal portraits and privacy, with false information and harmful content potentially causing further harm to individuals. Several methods have been proposed to protect faces from customization via adding protective noise to user images by disrupting the fine-tuned models.
Unfortunately, simple pre-processing techniques like JPEG compression, a normal pre-processing operation performed by modern social networks, can easily erase the protective effects of existing methods. To counter JPEG compression and other potential pre-processing, we propose GAP-Diff, a framework of Generating data with Adversarial Perturbations for text-to-image Diffusion models using unsupervised learning-based optimization, including three functional modules. Specifically, our framework learns robust representations against JPEG compression by backpropagating gradient information through a pre-processing simulation module while learning adversarial characteristics for disrupting fine-tuned text-to-image diffusion models. Furthermore, we achieve an adversarial mapping from clean images to protected images by designing adversarial losses against these fine-tuning methods and JPEG compression, with stronger protective noises within milliseconds. Facial benchmark experiments, compared to state-of-the-art protective methods, demonstrate that GAP-Diff significantly enhances the resistance of protective noise to JPEG compression, thereby better safeguarding user privacy and copyrights in the digital world.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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SESSION
Session 3D: AI Safety

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Authors, Creators & Presenters: Shuo Shao (Zhejiang University), Yiming Li (Zhejiang University), Hongwei Yao (Zhejiang University), Yiling He (Zhejiang University), Zhan Qin (Zhejiang University), Kui Ren (Zhejiang University)

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PAPER
Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature Attribution
Ownership verification is currently the most critical and widely adopted post-hoc method to safeguard model copyright. In general, model owners exploit it to identify whether a given suspicious third-party model is stolen from them by examining whether it has particular properties 'inherited' from their released models. Currently, backdoor-based model watermarks are the primary and cutting-edge methods to implant such properties in the released models. However, backdoor-based methods have two fatal drawbacks, including harmfulness and ambiguity. The former indicates that they introduce maliciously controllable misclassification behaviors ( backdoor) to the watermarked released models. The latter denotes that malicious users can easily pass the verification by finding other misclassified samples, leading to ownership ambiguity.

In this paper, we argue that both limitations stem from the 'zero-bit' nature of existing watermarking schemes, where they exploit the status (misclassified) of predictions for verification. Motivated by this understanding, we design a new watermarking paradigm "Explanation as a Watermark (EaaW)", that implants verification behaviors into the explanation of feature attribution instead of model predictions. Specifically, EaaW embeds a 'multi-bit' watermark into the feature attribution explanation of specific trigger samples without changing the original prediction. We correspondingly design the watermark embedding and extraction algorithms inspired by explainable artificial intelligence. In particular, our approach can be used for different tasks (image classification and text generation). Extensive experiments verify the effectiveness and harmlessness of our EaaW and its resistance to potential attacks.

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ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

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SESSION
Session 3D: Al Safety

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Authors, Creators & Presenters: Yutong Wu (Nanyang Technological University), Jie Zhang (Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore), Florian Kerschbaum (University of Waterloo), Tianwei Zhang (Nanyang Technological University)

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PAPER
THEMIS: Regulating Textual Inversion for Personalized Concept Censorship

Personalization has become a crucial demand in the Generative AI technology. As the pre-trained generative model (e.g., stable diffusion) has fixed and limited capability, it is desirable for users to customize the model to generate output with new or specific concepts. Fine-tuning the pre-trained model is not a promising solution, due to its high requirements of computation resources and data. Instead, the emerging personalization approaches make it feasible to augment the generative model in a lightweight manner. However, this also induces severe threats if such advanced techniques are misused by malicious users, such as spreading fake news or defaming individual reputations. Thus, it is necessary to regulate personalization models (i.e., achieve concept censorship) for their development and advancement. In this paper, we focus on the regulation of a popular personalization technique dubbed textbf{Textual Inversion (TI)}, which can customize Text-to-Image (T2I) generative models with excellent performance. TI crafts the word embedding that contains detailed information about a specific object. Users can easily add the word embedding to their local T2I model, like the public Stable Diffusion (SD) model, to generate personalized images. The advent of TI has brought about a new business model, evidenced by the public platforms for sharing and selling word embeddings (e.g., Civitai [1]). Unfortunately, such platforms also allow malicious users to misuse the word embeddings to generate unsafe content, causing damages to the concept owners. We propose THEMIS to achieve the personalized concept censorship. Its key idea is to leverage the backdoor technique for good by injecting positive backdoors into the TI embeddings. Briefly, the concept owner selects some sensitive words as triggers during the training of TI, which will be censored for normal use. In the subsequent generation stage, if a malicious user combines the sensitive words with the personalized embeddings as final prompts, the T2I model will output a pre-defined target image rather than images including the desired malicious content. To demonstrate the effectiveness of THEMIS, we conduct extensive experiments on the TI embeddings with Latent Diffusion and Stable Diffusion, two prevailing open-sourced T2I models. The results demonstrate that THEMIS is capable of preventing Textual Inversion from cooperating with sensitive words meanwhile guaranteeing its pristine utility. Furthermore, THEMIS is general to different uses of sensitive words, including different locations, synonyms, and combinations of sensitive words. It can also resist different types of potential and adaptive attacks. Ablation studies are also conducted to verify our design.

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