MTU Cybersecurity Colloquium

Presenter: Xu Zhou

GraphRAG advances retrieval-augmented generation (RAG) by structuring external knowledge as multi-scale knowledge graphs, enabling language models to integrate both broad context and granular details in their generation. While GraphRAG has demonstrated success across domains, its security implications remain largely unexplored. To bridge this gap, this work examines GraphRAG's vulnerability to poisoning attacks, uncovering an intriguing security paradox: existing RAG poisoning attacks are less effective under GraphRAG than conventional RAG, due to GraphRAG's graph-based indexing and retrieval; yet, the same features also create new attack surfaces. We present GRAGPOISON, a novel attack that exploits shared relations in the underlying knowledge graph to craft poisoning text capable of compromising multiple queries simultaneously. GRAGPOISON employs three key strategies: i) relation injection to introduce false knowledge, ii) relation enhancement to amplify poisoning influence, and iii) narrative generation to embed malicious content within coherent text. Empirical evaluation across diverse datasets and models shows that GRAGPOISON substantially outperforms existing attacks in terms of effectiveness (up to 98% success rate) and scalability (using less than 68% poisoning text) on multiple variants of GraphRAG. We also explore potential defensive measures and their limitations, identifying promising directions for future research.

Presenter: Manoj Kumal

AI agents that combine large language models with non-AI system components are rapidly emerging in real-world applications, offering unprecedented automation and flexibility. However, this unprecedented flexibility introduces complex security challenges that differ from those found in traditional software systems. This paper presents the first comprehensive systematization of knowledge on AI agent security, including an analysis of agents’ design space, attack landscape, and defense mechanisms for secure AI agent systems. We further identify open challenges that point to promising directions for future research in this emerging domain. Our work intro- duces the first systematic framework for understanding the security risks and defense landscapes of AI agents, serving as a foundation for building both secure agentic systems and advancing research in this critical area.

Presenter: Haoyang Chen

Large language models (LLMs) have seen significant advancements, achieving superior performance in various Natural Language Processing (NLP) tasks. However, they remain vulnerable to backdoor attacks, where models behave normally for standard queries but generate harmful responses or unintended output when specific triggers are activated. Existing backdoor defenses either lack comprehensiveness in practice, focusing on narrow trigger settings, detection-only mechanisms, and limited domains, or fail to withstand advanced scenarios like model-editing-based, multi-trigger, and triggerless attacks. In this paper, we present LETHE, a novel method to eliminate backdoor behaviors from LLMs through knowledge dilution using both internal and external mechanisms. Internally, LETHE leverages a lightweight dataset to train a clean model, which is then merged with the backdoored model to neutralize malicious behaviors by diluting the backdoor impact within the model’s parametric memory. Externally, LETHE incorporates benign and semantically relevant evidence into the prompt to distract LLM’s attention from backdoor features. Experimental results on classification and generation domains across 5 widely used LLMs demonstrate that LETHE outperforms 8 state-of-the-art defense baselines against 8 backdoor attacks. LETHE reduces the attack success rate of advanced backdoor attacks by up to 98% while maintaining model utility. Furthermore, LETHE has proven to be cost-efficient and robust against adaptive backdoor attacks. The code is provided at https://github.com/Xxxxsir/Lethe  Disclaimer: This paper contains potentially offensive content.

Presenter: Haoyang Chen

Fusing visual understanding into language generation, Multimodal Large Language Models (MLLMs) are revolutionizing visual-language applications. Yet, these models are often plagued by the hallucination problem, which involves generating inaccurate objects, attributes, and relationships that do not match the visual content. In this work, we delve into the internal attention mechanisms of MLLMs to reveal the underlying causes of hallucination, exposing the inherent vulnerabilities in the instruction-tuning process. We propose a novel hallucination attack against MLLMs that exploits attention sink behaviors to trigger hallucinated content with minimal image-text relevance, posing a significant threat to critical downstream applications. Distinguished from previous adversarial methods that rely on fixed patterns, our approach generates dynamic, effective, and highly transferable visual adversarial inputs, without sacrificing the quality of model responses. Comprehensive experiments on 6 prominent MLLMs demonstrate the efficacy of our attack in compromising black-box MLLMs even with extensive mitigating mechanisms, as well as the promising results against cutting-edge commercial APIs, such as GPT-4o and Gemini 1.5. Our code is available at https://huggingface.co/RachelHGF/Mirage-in-the-Eyes.

Ransomware attacks have surged dramatically in recent years, causing billions of dollars in damages annually, yet remain challenging to detect. Since the emergence of the AIDS Trojan in 1989, researchers have developed various defense mechanisms to combat ransomware. Existing countermeasures employ multi-layered, proactive defense strategies integrating behavioral monitoring, decoy traps, immutable backups, zero-trust segmentation, and continuous user training (Kapoor et al., 2022). However, these approaches exhibit critical limitations: high false-positive rates (FPR) in anomaly detection, prohibitive computational costs on resource-constrained devices for real-time detection, and inability to counter zero-day exploits or sophisticated attack variants. To address these challenges, we propose SwiftRD, a high-performance detection model that combines pattern matching with lightweight large language models (LLMs) to capture distinctive I/O behavioral patterns of ransomware, enabling real-time detection. To ensure robustness and practical applicability, we created a comprehensive dataset captured at the Flash Translation Layer (FTL) level. Evaluated on I/O traces from real-world ransomware samples, SwiftRD achieves 99.03% accuracy with only 1.0% false-positive rate, significantly outperforming existing methods in both detection efficacy and response time.

Large Language Models (LLMs) demonstrate impressive capabilities across many applications but remain vulnerable to jailbreak attacks, which elicit harmful or unintended content. While model fine-tuning is an option for safety alignment, it is costly and prone to catastrophic forgetting. Prompt optimization has emerged as a promising alternative, yet existing prompt-based defenses typically rely on static modifications (e.g., fixed prefixes or suffixes) that cannot adapt to diverse and evolving attacks. We propose Dynamic Deep Prompt Optimization (DDPO), the first jailbreak defense based on deep prompt optimization. DDPO uses the target LLM’s own intermediate layers as feature extractors to dynamically generate defensive embeddings via a lightweight multilayer perceptron. These tailored embeddings are then injected into a subsequent intermediate layer, enabling an input-dependent defense without modifying the LLM’s weights. This design ensures high adaptability with minimal computational overhead. Experiments on a diverse set of models and attacks demonstrate that DDPO significantly outperforms static prompt optimization methods, particularly on weakly aligned models and when handling semantically ambiguous benign prompts, successfully distinguishing them from genuinely harmful requests.

Presenter: Caleb Rother

Presenter: Qiang Han

Automated program repair (APR) techniques, which aim to triage and fix software bugs autonomously, have emerged as powerful tools against vulnerable code. Recent advancements in large language models (LLMs) have further shown promising results when applied to APR, especially on patch generation. However, without effective fault localization and patch validation, APR tools specialized in patching alone cannot handle a more practical and end-to-end setting—given a concrete input that triggers a vulnerability, how to patch the program without breaking existing tests? 
In this paper, we introduce PATCHAGENT, a novel LLMbased APR tool that seamlessly integrates fault localization, patch generation, and validation within a single autonomous agent. PATCHAGENT employs a language server, a patch verifier, and interaction optimization techniques to mimic human-like reasoning during vulnerability repair. Evaluated on a dataset of 178 real-world vulnerabilities, PATCHAGENT successfully repairs over 90% of the cases, outperforming state-of-the-art APR tools where applicable. Our ablation study further offer insights into the how various interaction optimizations contribute to PATCHAGENT’s effectiveness.

Presenter: Amirmehdi Moghadamfarid

Although outsourcing data to decentralized cloud storage has many advantages, it also raises severe security concerns. For example, untrusted peers may discard data intentionally to save storage space or resell the space; the storage peers are not protected by experienced technicians and hence may be easily compromised by hackers or malware from outside. To mitigate those security concerns, data can be replicated among peers, and smart contracts running on the blockchain can be used to perform periodic data auditing; once any corruptions are detected, the replicated data can be used to repair the corruptions. However, this approach suffers from the extensive communication overhead imposed on the cloud system over time. In this work, we have designed HiDCE, a hardware-assisted integrity-assured decentralized cloud storage system based on erasure coding. At its core, HiDCE has leveraged two trusted hardware components equipped in storage peers, namely the flash translation layer (FTL) and the trusted execution environment (TEE), allowing the implementation of three new functionalities: self-audit, self-repair, and distributed repair. Our design can ensure the integrity and reliability of the erasure-coding-based decentralized cloud storage system with minimal communication requirements between peers while eliminating the reliance on trusted entities such as the blockchain. In addition, our design can efficiently mitigate generation and outsourcing attacks by collaborating with TEE and FTL. The security analysis and experimental evaluation justify that HiDCE can ensure the reliability of outsourced data in the erasure-coding-based decentralized cloud with minimal performance impact.

Presenter: Xinyun Liu

In the era of AI-generated content (AIGC), malicious tampering poses imminent threats to copyright integrity and information security. Current deep image watermarking, while widely accepted for safeguarding visual content, can only protect copyright and ensure traceability. They fall short in localizing increasingly realistic image tampering, potentially leading to trust crises, privacy violations, and legal disputes. To solve this challenge, we propose an innovative proactive forensics framework EditGuard, to unify copyright protection and tamper-agnostic localization, especially for AIGC-based editing methods. It can offer a meticulous embedding of imperceptible watermarks and precise decoding of tampered areas and copyright information. Leveraging our observed fragility and locality of image-into-image steganography, the realization of Edit-
This work was supported by National Natural Science Foundation of China under Grant 62372016. (B Corresponding author: Jian Zhang.) Guard can be converted into a united image-bit steganography issue, thus completely decoupling the training process from the tampering types. Extensive experiments verify that our EditGuard balances the tamper localization accuracy, copyright recovery precision, and generalizability to various AIGC-based tampering methods, especially for image forgery that is difficult for the naked eye to detect. 

Presenter: Shewei Ding

By locally encoding raw data into intermediate features, collaborative inference enables end users to leverage powerful deep learning models without exposure of sensitive raw data to cloud servers. However, recent studies have revealed that these intermediate features may not sufficiently preserve privacy, as information can be leaked, and raw data can be reconstructed via model inversion attacks (MIAs). Obfuscation-based methods, such as noise corruption, adversarial representation learning, and information filters, enhance the inversion robustness by obfuscating the task's irrelevant redundancy empirically. However, methods for quantifying such redundancy remain elusive, and the explicit mathematical relation between this redundancy minimization and inversion robustness enhancement has not yet been established. To address that, this work first theoretically proves that the conditional entropy of inputs given intermediate features provides a guaranteed lower bound on the reconstruction mean square error (MSE) under any MIA. Then, we derive a differentiable and solvable measure for bounding this conditional entropy based on the Gaussian mixture estimation and propose a conditional entropy maximization (CEM) algorithm to enhance the inversion robustness. Experimental results on four datasets demonstrate the effectiveness and adaptability of our proposed CEM; without compromising feature utility and computing efficiency, plugging the proposed CEM into obfuscation-based defense mechanisms consistently boosts their inversion robustness, achieving average gains ranging from 12.9% to 48.2%.

Past Colloquiums

Presenter: Zhuoyan Xu

Recent literature has shown that LLMs are vulnerable to backdoor attacks, where malicious attackers inject a secret token sequence (i.e., trigger) into training prompts and enforce their responses to include a specific target sequence. Unlike discriminative NLP models, which have a finite output space (e.g., those in sentiment analysis), LLMs are generative models, and their output space grows exponentially with the length of response, thereby posing significant challenges to existing backdoor detection techniques, such as trigger inversion. In this paper, we conduct a theoretical analysis of the LLM backdoor learning process under specific assumptions, revealing that the autoregressive training paradigm in causal language models inherently induces strong causal relationships among tokens in backdoor targets. We hence develop a novel LLM backdoor scanning technique, BAIT (Large Language Model Backdoor ScAnning by Inverting Attack Target). Instead of inverting back- door triggers like in existing scanning techniques for non-LLMs, BAIT determines if a model is backdoored by inverting back- door targets, leveraging the exceptionally strong causal relations among target tokens. BAIT substantially reduces the search space and effectively identifies backdoors without requiring any prior knowledge about triggers or targets. The search-based nature also enables BAIT to scan LLMs with only the black-box access. Evaluations on 153 LLMs with 8 architectures across 6 distinct attack types demonstrate that our method outperforms 5 baselines. Its superior performance allows us to rank at the top of the leaderboard in the LLM round of the TrojAI competition (a multi-year, multi-round backdoor scanning competition).

Presenter: Qiang Han

Presenter: Haoyang Chen

As face recognition is widely used in various security sensitive scenarios, face privacy issues are receiving increasing attention. Recently, many face recognition works have focused on privacy preservation and converted the original images into protected facial features. However, our study reveals that emerging Deep Learning-based (DL-based) reconstruction attacks exhibit notable ability in learning and removing the protection patterns introduced by existing schemes and recovering the original facial images, thus posing a significant threat to face privacy. To   address this threat, we introduce FaceObfuscator, a lightweight privacy-preserving face recognition system that first removes visual information that is non-crucial for face recognition from facial images via frequency domain and then generates obfuscated features interleaved in the feature space to resist gradient descent in DL-based reconstruction attacks. To minimize the loss in face recognition accuracy, obfuscated features with different identities are well-designed to be interleaved but non-duplicated in the feature space. This non-duplication ensures that FaceObfuscator can extract identity information from the obfuscated features for accurate face recognition. Extensive experimental results demonstrate that FaceObfuscator’s privacy protection capability improves around 90% compared to existing privacy-preserving methods in two major leakage scenarios including channel leakage and database leakage, with a negligible 0.3% loss in face recognition accuracy. Our approach has also been evaluated in a real-world environment and protected more than 100K people’ face data of a major university.

Presenter: Xinyun Liu

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 (i.e., 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 (i.e., misclassified) of predictions for verification. Motivated by this understanding, we design a new watermarking paradigm, i.e., 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 (e.g., image classification and text generation). Extensive experiments verify the effectiveness and harmlessness of our EaaW and its resistance to potential attacks.

Presenter - Caleb Rother

In the history of access control, nearly every system designed has relied on the operating system (OS) to enforce the access control protocols. However, if the OS (and specifically root access) is compromised, there are few if any solutions that can get users back into their system efficiently. In this work, we have proposed a novel approach that allows secure and efficient rollback of file access control after an adversary compromises the OS and corrupts the access control metadata. Our key observation is that the underlying flash memory typically performs out-of-place updates. Taking advantage of this unique feature, we can extract the "stale data" specific for OS access control, by performing low-level disk forensics over the raw flash memory. This allows efficiently rolling back the OS access control to a state pre-dating the compromise. To justify the feasibility of the proposed approach, we have implemented it in a computing device using file system EXT2/EXT3 and open-sourced flash memory firmware OpenNFM. We also evaluated the potential impact of our design on the original system. Experimental results indicate that the performance of the affected drive is not significantly impacted.

Presenter - Haoyang Chen

Deep Learning (DL) models have become crucial in digital transformation, thus raising concerns about their intellectual property rights. Different watermarking techniques have been developed to protect Deep Neural Networks (DNNs) from IP infringement, creating a competitive field for DNN water-marking and removal methods. The predominant watermarking schemes use white-box techniques, which involve modifying weights by adding a unique signature to specific DNN layers. On the other hand, existing attacks on whitebox watermarking usually require knowledge of the specific deployed watermarking scheme or access to the underlying data for further training and finetuning. We propose DeepEclipse, a novel and unified framework designed to remove white-box watermarks. We present obfuscation techniques that significantly differ from the existing whitebox watermarking removal schemes. DeepEclipse can evade watermark detection without prior knowledge of the underlying watermarking scheme, additional data, or training and fine-tuning. Our evaluation reveals that DeepEclipse excels in breaking multiple white-box watermarking schemes, reducing watermark detection to random guessing while maintaining a similar model accuracy as the original one. Our framework showcases a promising solution to address the ongoing DNN watermark protection and removal challenges.

Presenter - Xinyun Liu

Adversarial patch attacks present a signifcant threat to real-world object detectors due to their practical feasibility. Existing defense methods, which rely on attack data or prior knowledge, struggle to effectively address a wide range of adversarial patches. In this paper, we show two inherent characteristics of adversarial patches, semantic independence and spatial heterogeneity, independent of their appearance, shape, size, quantity, and location. Semantic independence indicates that adversarial patches operate autonomously within their semantic context, while spatial heterogeneity manifests as distinct image quality of the patch area that differs from original clean image due to the independent generation process. Based on these observations, we propose PAD, a novel adversarial patch localization and removal method that does not require prior knowledge or additional training. PAD offers patch-agnostic defense against various adversarial patches, compatible with any pre-trained object detectors. Our comprehensive digital and physical experiments involving diverse patch types, such as localized noise, printable, and naturalistic patches, exhibit notable improvements over state-of-the-art works. Our code is available at https://github.com/Lihua-Jing/PAD.

Presenter - Shiwei Ding

Nowadays, the deployment of deep learning-based applications is an essential task owing to the increasing demands on intelligent services. In this paper, we investigate latency attacks on deep learning applications. Unlike common adversarial attacks for misclassification, the goal of latency attacks is to increase the inference time, which may stop applications from responding to the requests within a reasonable time. This kind of attack is ubiquitous for various applications, and we use object detection to demonstrate how such kind of attacks work. We also design a framework named Overload to generate latency attacks at scale. Our method is based on a newly formulated optimization problem and a novel technique, called spatial attention. This attack serves to escalate the required computing costs during the inference time, consequently leading to an extended inference time for object detection. It presents a significant threat, especially to systems with limited computing resources. We conducted experiments using YOLOv5 models on Nvidia NX. Compared to existing methods, our method is simpler and more effective. The experimental results show that with latency attacks, the inference time of a single image can be increased ten times longer in reference to the normal setting. Moreover, our findings pose a potential new threat to all object detection tasks requiring non-maximum suppression (NMS), as our attack is NMS-agnostic.

Presenter - Dani Obidov

Because "out-of-the-box" large language models are capable of generating a great deal of objectionable content, recent work has focused on aligning these models in an attempt to prevent undesirable generation. While there has been some success at circumventing these measures - so-called "jailbreaks" against LLMs - these attacks have required significant human ingenuity and are brittle in practice. Attempts at automatic adversarial prompt generation have also achieved limited success. In this paper, we propose a simple and effective attack method that causes aligned language models to generate objectionable behaviors. Specifically, our approach finds a suffix that, when attached to a wide range of queries for an LLM to produce objectionable content, aims to maximize the probability that the model produces an affirmative response (rather than refusing to answer). However, instead of relying on manual engineering, our approach automatically produces these adversarial suffixes by a combination of greedy and gradient-based search techniques, and also improves over past automatic prompt generation methods. Surprisingly, we find that the adversarial prompts generated by our approach are highly transferable, including to black-box, publicly released, production LLMs. Specifically, we train an adversarial attack suffix on multiple prompts (i.e., queries asking for many different types of objectionable content), as well as multiple models (in our case, Vicuna-7B and 13B). When doing so, the resulting attack suffix induces objectionable content in the public interfaces to ChatGPT, Bard, and Claude, as well as open source LLMs such as LLaMA-2-Chat, Pythia, Falcon, and others. Interestingly, the success rate of this attack transfer is much higher against the GPT-based models, potentially owing to the fact that Vicuna itself is trained on outputs from ChatGPT. In total, this work significantly advances the state-of the-art in adversarial attacks against aligned language models, raising important questions about how such systems can be prevented from producing objectionable information. Code is available at github.com/llm-attacks/llm-attacks.

Presenter - Harsh Singh

The increasing popularity and deployment of Internet of Vehicles (IoV) along with Connected Autonomous Vehicles (CAVs) have benefits ranging from enhanced road safety to efficient traffic management. However, with the increasing connectivity and data exchange among vehicles, the security of IoV systems becomes paramount. In this paper, I have proposed a novel approach for ensuring secure access control within IoV environments using the Proof of Elapsed Time (PoET) consensus mechanism of Blockchain. PoET, a lightweight consensus algorithm based on trusted execution environments (TEE), offers a robust solution for securely managing access permissions in dynamic IoV scenarios. A detailed analysis of the proposed system architecture, highlighting its resilience against various security threats including unauthorized access, data tampering, and Sybil attacks, has also been discussed. Additionally, the implementation aspects and evaluation of the performance of the solution through simulations and real-world experiments have been showcased. The findings demonstrate that the integration of PoET into IoV environments significantly enhances security without compromising efficiency, making it a promising solution for securing the next generation of connected and autonomous vehicles (CAVs).

Presenter - Caleb Rother

In the history of access control, nearly every system designed has relied on the operating system for enforcement of its protocols. If the operating system (and specifically root access) is compromised, there are few if any solutions that can get users back into their system efficiently. In this work, we have proposed a method by which file permissions can be efficiently rolled back after a catastrophic failure of permission enforcement. Our key idea is to leverage the out-of-place-update feature of flash memory in order to collaborate with the flash translation layer to efficiently return those permissions to a state pre-dating the failure.

Presenter - Shiwei Ding

The backdoor attack, where the adversary uses inputs stamped with triggers (e.g., a patch) to activate pre-planted malicious behaviors, is a severe threat to Deep Neural Network (DNN) models. Trigger inversion is an effective way of identifying backdoor models and understanding embedded adversarial behaviors. Achallenge of trigger inversion is that there are many ways of constructing the trigger. Existing methods cannot generalize to various types of triggers by making certain assumptions or attack-specific constraints. The fundamental reason is that existing work does not consider the trigger’s design space in their formulation of the inversion problem. This work formally defines and analyzes the triggers injected in different spaces and the inversion problem. Then, it proposes a unified framework to invert backdoor triggers based on the formalization of triggers and the identified inner behaviors of backdoor models from our analysis. Our prototype UNICORN is general and effective in inverting backdoor triggers in DNNs.

Presenter - Haoyang Chen

The fast-growing surveillance systems will make image captioning, i.e., automatically generating text descriptions of images, an essential technique to process the huge volumes of videos efficiently, and correct captioning is essential to ensure the text authenticity. While prior work has demonstrated the feasibility of fooling computer vision models with adversarial patches, it is unclear whether the vulnerability can lead to incorrect captioning, which involves natural language processing after image feature extraction. In this paper, we design CAPatch, a physical adversarial patch that can result in mistakes in the final captions, i.e., either create a completely different sentence or a sentence with keywords missing, against multi-modal image captioning systems. To make CAPatch effective and practical in the physical world, we propose a detection assurance and attention enhancement method to increase the impact of CAPatch and a robustness improvmment method to address the patch distortions caused by image printing and capturing. Evaluations on three commonly used image captioning systems (Show-and-Tell, Self-critical Sequence Training: Att2in, and Bottom-up Top-down) demonstrate the effectiveness of CAPatch in both the digital and physical worlds, whereby volunteers wear printed patches in various scenarios, clothes, lighting conditions. With a size of 5% of the image, physically printed CAPatch can achieve continuous attacks with an attack success rate higher than 73.1% over a video recorder.

Presenter- Doni Obidov

Abstract:

In the rapidly evolving domain of language model (LM) development, ensuring the integrity and security of training datasets is crucial. This study introduces a sophisticated form of data poisoning, categorized as a backdoor attack, which subtly undermines LMs. Diverging from traditional methodologies that rely on textual alterations within the training corpus, our approach is grounded in the strategic manipulation of labels for a select subset of the dataset. This discreet yet potent form of attack demonstrates its efficacy through the implementation of both single-word and multi-word trigger mechanisms. The novelty of our method lies in its unobtrusiveness, effectively executing backdoor attacks without the need for conspicuous text modifications. Our findings reveal significant vulnerabilities in LM training processes, underscoring the need for enhanced security measures in dataset preparation and model training. This paper not only elucidates the feasibility of label-based backdoor attacks but also serves as a crucial reminder of the often-overlooked subtleties in dataset security that can have profound implications on model integrity.

Presenter- Haoyang Chen

Abstract:

Adversarial purification refers to a class of defense methods that remove adversarial perturbations using a generative model. These methods do not make assumptions on the form of attack and the classification model, and thus can defend pre-existing classifiers against unseen threats. However, their performance currently falls behind adversarial training methods. In this work, we propose DiffPure that uses diffusion models for adversarial purification: Given an adversarial example, we first diffuse it with a small amount of noise following a forward diffusion process, and then recover the clean image through a reverse generative process. To evaluate our method against strong adaptive attacks in an efficient and scalable way, we propose to use the adjoint method to compute full gradients of the reverse generative process. Extensive experiments on three image datasets including CIFAR10, ImageNet and CelebA-HQ with three classifier architectures including ResNet, WideResNet and ViT demonstrate that our method achieves the state-of-the-art results, outperforming current adversarial training and adversarial purification methods, often by a large margin.

Presenter- Niusen Chen

Abstract:

With increasing development of connected and autonomous vehicles, the risk of cyber threats on them is also increasing. Compared to traditional computer systems, a CAV attack is more critical, as it does not only threaten confidential data or system access, but may endanger the lives of drivers and passengers. To control a vehicle, the attacker may inject malicious control messages into the vehicle’s controller area network. To make this attack persistent, the most reliable method is to inject malicious code into an electronic control unit’s firmware. This allows the attacker to inject CAN messages and exhibit significant control over the vehicle, posing a safety threat to anyone in proximity. In this work, we have designed a defensive framework which allows restoring compromised ECU firmware in real-time. Our framework combines existing intrusion detection methods with a 'firmware recovery mechanism using trusted hardware components equipped in ECUs. Especially,the firmware restoration utilizes the existing FTL in the flash storage device. This process is highly efficient by minimizing the necessary restored information. Further, the recovery is managed via a trusted application running in TrustZone secure world. Both the FTL and TrustZone are secure when the ECU firmware is compromised. Steganography is used to hide communications during recovery. We have implemented and evaluated our prototype implementation in a testbed simulating the real-world in-vehicle scenario.

Presenter - Dr. Ronghua Xu

Abstract:

The fast integration of the fifth-generation (5G) communication, Artificial Intelligence (AI), and the Internet of Things (IoT) technologies is envisioned to enable Next Generation Networks (NGNs) that provides diverse intelligent services for Smart Cities. However, the ubiquitous proliferation of highly connected end devices and user-defined applications bring serious services provisioning, security, privacy, and management challenges on the centralized framework adopted by conventional networking systems. My research aims for a large-dimensional, autonomous and intelligent network infrastructure that integrates Machine Learning (ML), Blockchain, and Network Slicing (NS) atop of the sixth-generation (6G) communication networks to provide decentralized, secure, scalable, resilient, and efficient network services and a dynamic resource management for complex and heterogeneous IoT ecosystems, like Metaverse, smart transportation, and Unmanned Aerial Vehicle (UAV) systems, etc. Therefore, this presentation will introduce "a Secure-by-Design Federated Microchain Fabric for Internet-of-Things (IoT) System", which laid down a solid foundation for constructing the secure and decentralized networking infrastructure under multi-domain IoT scenarios. From the system architecture aspect, I will specially focus on microDFL, which is a novel hierarchical IoT network fabric for decentralized federated learning (DFL) atop of the federation of lightweight Microchains. Under the framework of federated Microchain, I will explain two lightweight microchains for IoT systems, called Econledger and Fairledger, which adopt efficient consensus protocols to improve performance at the network of edge. Following that, a novel epoch randomness-enabled consensus committee configuration scheme call ECOM has been designed to enhance scalability and security of the small scale microchain, and a smart contract enabled inter-ledger protocol has been implemented to improve interoperation during cross-chain operations. After that, I will explain a novel concept of a dynamic edge resource federation framework by joint combination of a federated microchain fabric with network slicing technology, which shed the light on future opportunities that guarantee scalability, dynamicity, and security for multi-domain IoT ecosystems atop of NGNs. Moreover, I will also talk on applying of the key ideas of microchain into different IoT scenarios, like IoT network security, data marketplaces, and urban air mobility systems. Finally, I will conclude my talk by presenting vision towards NGNs that provide ubiquitous and pervasively network access for users.

Presenter - Dr. Xinyun Liu

Abstract:

A generative AI model can generate extremely realistic-looking content, posing growing challenged to the authenticity of information. To address the challenges, watermark has been leveraged to detect AI-generated content before it is released. Content is detected as AI-generated if a similar watermark can be decoded from it. In this work, we perform a systematic study on the robustness of such watermark-based AI-generative content detection. We focus on AI-generated images. Our work shows that an attacker can post-process a watermarked image via adding a small, human-imperceptible perturbation to it, such that the post-processed image evades detection while maintaining its visual quality. We show the effectiveness of our attack both theoretically and empirically. Moreover, to evade detection, our adversarial post-processing method adds much smaller perturbations to AI-generated images and thus better maintain their visual quality than existing popular post-processing methods such as JPEG compression Gaussian blur, and brightness/contrast. Our work shows the insufficiency of existing watermark-based detection of AI-generated content, highlighting the urgent needs of new methods.