| Total: 99
Android is by far the most popular OS with over three billion active mobile devices. As in any software, uncovering vulnerabilities on Android devices and applying timely patches are both critical. Android Open Source Project has initiated efforts to improve the traceability of security updates through Security Patch Levels assigned to devices. While this initiative provided better traceability for the vulnerabilities, it has not entirely resolved the issues related to the timeliness and availability of security updates for end users. Recent studies on Android security updates have focused on the issue of delay during the security update roll-out, largely attributing this to factors related to fragmentation. However, these studies fail to capture the entire Android ecosystem as they primarily examine flagship devices or do not paint a comprehensive picture of the Android devices’ lifecycle due to the datasets spanning over a short timeframe. To address this gap in the literature, we utilize a device-centric approach to analyze the security update behavior of Android devices. Our approach aims to understand the security update distribution behavior of Original Equipment Manufacturers (OEM) by using a representative set of devices from each OEM and characterize the complete lifecycle of an average Android device. We obtained 367K official security update records from public sources, spanning from 2014 to 2023. Our dataset contains 599 unique devices from four major OEMs that are used in 97 countries and are associated with 109 carriers. We identify significant differences in the roll-out of security updates across different OEMs, device models and types, and geographical regions across the world. Our findings show that the reasons for the delay in the roll-out of security updates are not limited to fragmentation but also involve several OEM-specific factors such as the type of support the device receives (e.g., monthly, quarterly, biannual). Our analysis also uncovers certain key issues regarding the security update distribution that can be readily addressed as well as exemplary practices that can be immediately adopted by OEMs in practice.
Over the past several years, the mobile security community has discovered a wide variety of exploits against link and session-establishment protocols. These exploits can be implemented on software-defined radios (SDRs) that disrupt, spoof, or flood layer-3 (L3) messages to compromise security and privacy, which still apply to the latest 5G mobile network standard. Interestingly, unlike the prior generations of closed (proprietary) mobile network infrastructures, 5G networks are migrating toward a more intelligent and open-standards-based fully interoperable mobile architecture, called Open RAN or O-RAN. The implications of transitioning mobile infrastructures to a software-defined architectural abstraction are quite significant to the INFOSEC community, as it allows us to extend the mobile data plane and control plane with security-focused protocol auditing services and exploit detection. Based on this design, we present 5G-SPECTOR, the first comprehensive framework for detecting the wide spectrum of L3 protocol exploits on O-RAN. It features a novel security audit stream called MOBIFLOW that transfers fine-grained cellular network telemetry, and a programmable control-plane xApp called MOBIEXPERT. We present an extensible prototype of 5G-SPECTOR which can detect 7 types of cellular attacks in real time. We also demonstrate its scalability to 11 unknown attacks as well as 31 real-world cellular traces, with effective performance (high accuracy, no false alarms) and low (<2% CPU, <100 MB memory) overhead.
The right to be forgotten requires the removal or "unlearning" of a user's data from machine learning models. However, in the context of Machine Learning as a Service (MLaaS), retraining a model from scratch to fulfill the unlearning request is impractical due to the lack of training data on the service provider's side (the server). Furthermore, approximate unlearning further embraces a complex trade-off between utility (model performance) and privacy (unlearning performance). In this paper, we try to explore the potential threats posed by unlearning services in MLaaS, specifically over-unlearning, where more information is unlearned than expected. We propose two strategies that leverage over-unlearning to measure the impact on the trade-off balancing, under black-box access settings, in which the existing machine unlearning attacks are not applicable. The effectiveness of these strategies is evaluated through extensive experiments on benchmark datasets, across various model architectures and representative unlearning approaches. Results indicate significant potential for both strategies to undermine model efficacy in unlearning scenarios. This study uncovers an underexplored gap between unlearning and contemporary MLaaS, highlighting the need for careful considerations in balancing data unlearning, model utility, and security.
The FIDO2 protocol aims to strengthen or replace password authentication using public-key cryptography. FIDO2 has primarily focused on defending against attacks from afar by remote attackers that compromise a password or attempt to phish the user. In this paper, we explore threats from local attacks on FIDO2 that have received less attention---a malicious browser extension or cross-site scripting (XSS), and attackers gaining physical access to an HSK. Our systematic analysis of current implementations of FIDO2 reveals four underlying flaws, and we demonstrate the feasibility of seven attacks that exploit those flaws. The flaws include (1) Lack of confidentiality/integrity of FIDO2 messages accessible to browser extensions, (2) Broken clone detection algorithm, (3) Potential for user misunderstanding from social engineering and notification/error messages, and (4) Cookie life cycle. We build malicious browser extensions and demonstrate the attacks on ten popular web servers that use FIDO2. We also show that many browser extensions have sufficient permissions to conduct the attacks if they were compromised. A static and dynamic analysis of current browser extensions finds no evidence of the attacks in the wild. We conducted two user studies confirming that participants do not detect the attacks with current error messages, email notifications, and UX responses to the attacks. We provide an improved clone detection algorithm and recommendations for relying parties that detect or prevent some of the attacks.
Sharding is a critical technique that enhances the scalability of blockchain technology. However, existing protocols often assume adversarial nodes in a general term without considering the different types of attacks, which limits transaction throughput at runtime because attacks on liveness could be mitigated. There have been attempts to increase transaction throughput by separately handling the attacks; however, they have security vulnerabilities. This paper introduces Reticulum, a novel sharding protocol that overcomes these limitations and achieves enhanced scalability in a blockchain network without security vulnerabilities. Reticulum employs a two-phase design that dynamically adjusts transaction throughput based on runtime adversarial attacks on either or both liveness and safety. It consists of 'control' and 'process' shards in two layers corresponding to the two phases. Process shards are subsets of control shards, with each process shard expected to contain at least one honest node with high confidence. Conversely, control shards are expected to have a majority of honest nodes with high confidence. Reticulum leverages unanimous voting in the first phase to involve fewer nodes in accepting/rejecting a block, allowing more parallel process shards. The control shard finalizes the decision made in the first phase and serves as a lifeline to resolve disputes when they surface. Experiments demonstrate that the unique design of Reticulum empowers high transaction throughput and robustness in the face of different types of attacks in the network, making it superior to existing sharding protocols for blockchain networks.
WireGuard is a Virtual Private Network (VPN), presented at NDSS 2017, recently integrated into the Linux Kernel and paid commercial VPNs such as NordVPN, Mullvad and ProtonVPN. It proposes a different approach from other classical VPN such as IPsec or OpenVPN because it does not let configure cryptographic algorithms. The protocol inside WireGuard is a dedicated extension of IKpsk2 protocol from Noise Framework. Different analyses of WireGuard and IKpsk2 protocols have been proposed, in both the symbolic and the computational model, with or without computer-aided proof assistants. These analyses however consider different adversarial models or refer to incomplete versions of the protocols. In this work, we propose a unified formal model of WireGuard protocol in the symbolic model. Our model uses the automatic cryptographic protocol verifiers SAPIC+, ProVerif and Tamarin. We consider a complete protocol execution, including cookie messages used for resistance against denial of service attacks. We model a precise adversary that can read or set static, ephemeral or pre-shared keys, read or set ECDH pre-computations, control key distribution. Eventually, we present our results in a unified and interpretable way, allowing comparisons with previous analyses. Finally thanks to our models, we give necessary and sufficient conditions for security properties to be compromised, we confirm a flaw on the anonymity of the communications and point an implementation choice which considerably weakens its security. We propose a remediation that we prove secure using our models.
Mobile tracking has long been a privacy problem, where the geographic data and timestamps gathered by mobile network operators (MNOs) are used to track the locations and movements of mobile subscribers. Additionally, selling the geolocation information of subscribers has become a lucrative business. Many mobile carriers have violated user privacy agreements by selling users' location history to third parties without user consent, exacerbating privacy issues related to mobile tracking and profiling. This paper presents AAKA, an anonymous authentication and key agreement scheme designed to protect against mobile tracking by honest-but-curious MNOs. AAKA leverages anonymous credentials and introduces a novel mobile authentication protocol that allows legitimate subscribers to access the network anonymously, without revealing their unique (real) IDs. It ensures the integrity of user credentials, preventing forgery, and ensures that connections made by the same user at different times cannot be linked. While the MNO alone cannot identify or profile a user, AAKA enables identification of a user under legal intervention, such as when the MNOs collaborate with an authorized law enforcement agency. Our design is compatible with the latest cellular architecture and SIM standardized by 3GPP, meeting 3GPP's fundamental security requirements for User Equipment (UE) authentication and key agreement processes. A comprehensive security analysis demonstrates the scheme's effectiveness. The evaluation shows that the scheme is practical, with a credential presentation generation taking ~52 ms on a constrained host device equipped with a standard cellular SIM.
Smart contracts play a vital role in the Ethereum ecosystem. Due to the prevalence of kinds of security issues in smart contracts, the smart contract verification is urgently needed, which is the process of matching a smart contract’s source code to its on-chain bytecode for gaining mutual trust between smart contract developers and users. Although smart contract verification services are embedded in both popular Ethereum browsers (e.g., Etherscan and Blockscout) and official platforms (i.e., Sourcify), and gain great popularity in the ecosystem, their security and trustworthiness remain unclear. To fill the void, we present the first comprehensive security analysis of smart contract verification services in the wild. By diving into the detailed workflow of existing verifiers, we have summarized the key security properties that should be met, and observed eight types of vulnerabilities that can break the verification. Further, we propose a series of detection and exploitation methods to reveal the presence of vulnerabilities in the most popular services, and uncover 19 exploitable vulnerabilities in total. All the studied smart contract verification services can be abused to help spread malicious smart contracts, and we have already observed the presence of using this kind of tricks for scamming by attackers. It is hence urgent for our community to take actions to detect and mitigate security issues related to smart contract verification, a key component of the Ethereum smart contract ecosystem.
Smart Televisions (TVs) are internet-connected TVs that support video streaming applications and web browsers. Users enter information into Smart TVs through on-screen virtual keyboards. These keyboards require users to navigate between keys with directional commands from a remote controller. Given the extensive functionality of Smart TVs, users type sensitive information (e.g., passwords) into these devices, making keystroke privacy necessary. This work develops and demonstrates a new side-channel attack that exposes keystrokes from the audio of two popular Smart TVs: Apple and Samsung. This side-channel attack exploits how Smart TVs make different sounds when selecting a key, moving the cursor, and deleting a character. These properties allow an attacker to extract the number of cursor movements between selections from the TV's audio. Our attack uses this extracted information to identify the likeliest typed strings. Against realistic users, the attack finds up to 33.33% of credit card details and 60.19% of common passwords within 100 guesses. This vulnerability has been acknowledged by Samsung and highlights how Smart TVs must better protect sensitive data.
Well-trained deep neural network (DNN) models can be treated as commodities for commercial transactions and generate significant revenues, raising the urgent need for intellectual property (IP) protection against illegitimate reproducing. Emerging studies on IP protection often aim at inserting watermarks into DNNs, allowing owners to passively verify the ownership of target models after counterfeit models appear and commercial benefits are infringed, while active authentication against unauthorized queries of DNN-based applications is still neglected. In this paper, we propose a novel approach to protect model intellectual property, called ActiveDaemon, which incorporates a built-in access control function in DNNs to safeguard against commercial piracy. Specifically, our approach enables DNNs to predict correct outputs only for authorized users with user-specific tokens while producing poor accuracy for unauthorized users. In ActiveDaemon, the user-specific tokens are generated by a specially designed U-Net style encoder-decoder network, which can map strings and input images into numerous noise images to address identity management with large-scale user capacity. Compared to existing studies, these user-specific tokens are invisible, dynamic and more perceptually concealed, enhancing the stealthiness and reliability of model IP protection. To automatically wake up the model accuracy, we utilize the data poisoning-based training technique to unconsciously embed the ActiveDaemon into the neuron's function. We conduct experiments to compare the protection performance of ActiveDaemon with four state-of-the-art approaches over four datasets. The experimental results show that ActiveDaemon can reduce the accuracy of unauthorized queries by as much as 81% with less than a 1.4% decrease in that of authorized queries. Meanwhile, our approach can also reduce the LPIPS scores of the authorized tokens to 0.0027 on CIFAR10 and 0.0368 on ImageNet.
Private Set Intersection (PSI) is a widely used protocol that enables two parties to securely compute a function over the intersected part of their shared datasets and has been a significant research focus over the years. However, recent studies have highlighted its vulnerability to Set Membership Inference Attacks (SMIA), where an adversary might deduce an individual's membership by invoking multiple PSI protocols. This presents a considerable risk, even in the most stringent versions of PSI, which only return the cardinality of the intersection. This paper explores the evaluation of anonymity within the PSI context. Initially, we highlight the reasons why existing works fall short in measuring privacy leakage, and subsequently propose two attack strategies that address these deficiencies. Furthermore, we provide theoretical guarantees on the performance of our proposed methods. In addition to these, we illustrate how the integration of auxiliary information, such as the sum of payloads associated with members of the intersection (PSI-SUM), can enhance attack efficiency. We conducted a comprehensive performance evaluation of various attack strategies proposed utilizing two real datasets. Our findings indicate that the methods we propose markedly enhance attack efficiency when contrasted with previous research endeavors. The effective attacking implies that depending solely on existing PSI protocols may not provide an adequate level of privacy assurance. It is recommended to combine privacy-enhancing technologies synergistically to enhance privacy protection even further.
Federated Learning (FL) enables the training of machine learning models using distributed data. This approach offers benefits such as improved data privacy, reduced communication costs, and enhanced model performance through increased data diversity. However, FL systems are vulnerable to poisoning attacks, where adversaries introduce malicious updates to compromise the integrity of the aggregated model. Existing defense strategies against such attacks include filtering, influence reduction, and robust aggregation techniques. Filtering approaches have the advantage of not reducing classification accuracy, but face the challenge of adversaries adapting to the defense mechanisms. The lack of a universally accepted definition of "adaptive adversaries" in the literature complicates the assessment of detection capabilities and meaningful comparisons of FL defenses. In this paper, we address the limitations of the commonly used definition of "adaptive attackers" proposed by Bagdasaryan et al. We propose AutoAdapt, a novel adaptation method that leverages an Augmented Lagrangian optimization technique. AutoAdapt eliminates the manual search for optimal hyper-parameters by providing a more rational alternative. It generates more effective solutions by accommodating multiple inequality constraints, allowing adaptation to valid value ranges within the defensive metrics. Our proposed method significantly enhances adversaries' capabilities and accelerates research in developing attacks and defenses. By accommodating multiple valid range constraints and adapting to diverse defense metrics, AutoAdapt challenges defenses relying on multiple metrics and expands the range of potential adversarial behaviors. Through comprehensive studies, we demonstrate the effectiveness of AutoAdapt in simultaneously adapting to multiple constraints and showcasing its power by accelerating the performance of tests by a factor of 15. Furthermore, we establish the versatility of AutoAdapt across various application scenarios, encompassing datasets, model architectures, and hyper-parameters, emphasizing its practical utility in real-world contexts. Overall, our contributions advance the evaluation of FL defenses and drive progress in this field.
Java deserialization vulnerabilities have long been a grave security concern for Java applications. By injecting malicious objects with carefully crafted structures, attackers can reuse a series of existing methods during deserialization to achieve diverse attacks like remote code execution. To mitigate such attacks, developers are encouraged to implement policies restricting the object types that applications can deserialize. However, the design of precise policies requires expertise and significant manual effort, often leading to either the absence of policy or the implementation of inadequate ones. In this paper, we propose DeseriGuard, a tool designed to assist developers in securing their applications seamlessly against deserialization attacks. It can automatically formulate a policy based on the application's semantics and then enforce it to restrict illegal deserialization attempts. First, DeseriGuard utilizes dataflow analysis to construct a semantic-aware property tree, which records the potential structures of deserialized objects. Based on the tree, DeseriGuard identifies the types of objects that can be safely deserialized and synthesizes an allowlist policy. Then, with the Java agent, DeseriGuard can seamlessly enforce the policy during runtime to protect various deserialization procedures. In evaluation, DeseriGuard successfully blocks all deserialization attacks on 12 real-world vulnerabilities. In addition, we compare DeseriGuard's automatically synthesized policies with 109 developer-designed policies. The results demonstrate that DeseriGuard effectively restricts 99.12% more classes. Meanwhile, we test the policy-enhanced applications with their unit tests and integration tests, which demonstrate that DeseriGuard's policies will not interfere with applications' execution and induce a negligible time overhead of 2.17%.
Overlay is a notable user interface feature in the Android system, which allows an app to draw over other apps' windows. While overlay enhances user experience and allows concurrent app interaction, it has been extensively abused for malicious purposes, such as "tapjacking", leading to so-called overlay attacks. In order to combat this threat, Google introduced a dedicated window flag SYSTEM_FLAG_HIDE_NON_SYSTEM_OVERLAY_WINDOWS to protect critical system apps' windows against overlay attacks. Unfortunately, the adequacy of such protection in the Android system remains unstudied, with a noticeable absence of clear usage guidelines. To bridge the gap, in this paper, we conduct the first systematic study on the unprotected windows of system apps against overlay attacks. We propose a comprehensive guideline and then design and develop a new tool named OverlayChecker to identify the missing protections in Android system apps. To verify the uncovered issues, we also design and create Proof-of-Concept apps. After applying OverlayChecker to 8 commercial Android systems on 4 recently released Android versions, we totally discovered 49 vulnerable system apps' windows. We reported our findings to the mobile vendors, including Google, Samsung, Vivo, Xiaomi, and Honor. At the time of writing, 15 of them have been confirmed. 5 CVEs have been assigned, and 3 of them are rated high severity. We also received bug bounty rewards from these mobile vendors.
We present BGP-iSec, an enhancement of the BGPsec protocol for securing BGP, the Internet's inter-domain routing protocol. BGP-iSec ensures additional and stronger security properties, compared to BGPsec, without significant extra overhead. The main improvements are: (i) Security for partial adoption: BGP-iSec provides significant security benefits for early adopters, in contrast to BGPsec, which requires universal adoption. (ii) Defense against route leakage: BGP-iSec defends against route leakage, a common cause of misrouting that is not prevented by BGPsec. (iii) Integrity of attributes: BGP-iSec ensures the integrity of revertible attributes, thereby preventing announcement manipulation attacks not prevented by BGPsec. We show that BGP-iSec achieves these goals using extensive simulations as well as security analysis. The BGP-iSec design conforms, where possible, with the BGPsec design, modifying it only where necessary to improve security. By providing stronger security guarantees, especially for partial adoption, we hope BGP-iSec will be a step towards finally protecting inter-domain routing, which remains, for many years, a vulnerability of the Internet's infrastructure.
Outsourced computing is widely used today. However, current approaches for protecting client data in outsourced computing fall short: use of cryptographic techniques like fully-homomorphic encryption incurs substantial costs, whereas use of hardware-assisted trusted execution environments has been shown to be vulnerable to run-time and side-channel attacks. We present BliMe, an architecture to realize efficient and secure outsourced computation. BliMe consists of a novel and minimal set of instruction set architecture (ISA) extensions implementing a taint-tracking policy to ensure the confidentiality of client data even in the presence of server vulnerabilities. To secure outsourced computation, the BliMe extensions can be used together with an attestable, fixed-function hardware security module (HSM) and an encryption engine that provides atomic decrypt-and-taint and encrypt-and-untaint operations. Clients rely on remote attestation and key agreement with the HSM to ensure that their data can be transferred securely to and from the encryption engine and will always be protected by BliMe's taint-tracking policy while at the server. We provide an RTL implementation BliMe-BOOM based on the BOOM RISC-V core. BliMe-BOOM requires no reduction in clock frequency relative to unmodified BOOM, and has minimal power ($
Confidential computing is an emerging technique that provides users and third-party developers with an isolated and transparent execution environment. To support this technique, Arm introduced the Confidential Computing Architecture (CCA), which creates multiple isolated address spaces, known as realms, to ensure data confidentiality and integrity in security-sensitive tasks. Arm recently proposed the concept of confidential computing on GPU hardware, which is widely used in general-purpose, high-performance, and artificial intelligence computing scenarios. However, hardware and firmware supporting confidential GPU workloads remain unavailable. Existing studies leverage Trusted Execution Environments (TEEs) to secure GPU computing on Arm- or Intel-based platforms, but they are not suitable for CCA's realm-style architecture, such as using incompatible hardware or introducing a large trusted computing base (TCB). Therefore, there is a need to complement existing Arm CCA capabilities with GPU acceleration. To address this challenge, we present CAGE to support confidential GPU computing for Arm CCA. By leveraging the existing security features in Arm CCA, CAGE ensures data security during confidential computing on unified-memory GPUs, the mainstream accelerators in Arm devices. To adapt the GPU workflow to CCA's realm-style architecture, CAGE proposes a novel shadow task mechanism to manage confidential GPU applications flexibly. Additionally, CAGE leverages the memory isolation mechanism in Arm CCA to protect data confidentiality and integrity from the strong adversary. Based on this, CAGE also optimizes security operations in memory isolation to mitigate performance overhead. Without hardware changes, our approach uses the generic hardware security primitives in Arm CCA to defend against a privileged adversary. We present two prototypes to verify CAGE's functionality and evaluate performance, respectively. Results show that CAGE effectively provides GPU support for Arm CCA with an average of 2.45% performance overhead.
The proliferation of images captured from millions of cameras and the advancement of facial recognition (FR) technology have made the abuse of FR a severe privacy threat. Existing works typically rely on obfuscation, synthesis, or adversarial examples to modify faces in images to achieve anti-facial recognition (AFR). However, the unmodified images captured by camera modules that contain sensitive personally identifiable information (PII) could still be leaked. In this paper, we propose a novel approach, ***CamPro***, to capture inborn AFR images. ***CamPro*** enables well-packed commodity camera modules to produce images that contain little PII and yet still contain enough information to support other non-sensitive vision applications, such as person detection. Specifically, ***CamPro*** tunes the configuration setup inside the camera image signal processor (ISP), i.e., color correction matrix and gamma correction, to achieve AFR, and designs an image enhancer to keep the image quality for possible human viewers. We implemented and validated ***CamPro*** on a proof-of-concept camera, and our experiments demonstrate its effectiveness on ten state-of-the-art black-box FR models. The results show that ***CamPro*** images can significantly reduce face identification accuracy to 0.3% while having little impact on the targeted non-sensitive vision application. Furthermore, we find that ***CamPro*** is resilient to adaptive attackers who have re-trained their FR models using images generated by ***CamPro***, even with full knowledge of privacy-preserving ISP parameters.
The certificate transparency (CT) framework has been deployed to improve the accountability of the TLS certificate ecosystem. However, the current implementation of CT does not enforce or guarantee the correct behavior of third-party monitors, which are essential components of the CT framework, and raises security and reliability concerns. For example, recent studies reported that 5 popular third-party CT monitors cannot always return the complete set of certificates inquired by users, which fundamentally impairs the protection that CT aims to offer. This work revisits the CT design and proposes an additional component of the CT framework, CT watchers. A watcher acts as an inspector of third-party CT monitors to detect any misbehavior by inspecting the certificate search services of a third-party monitor and detecting any inconsistent results returned by multiple monitors. It also semi-automatically analyzes potential causes of the inconsistency, e.g., a monitor’s misconfiguration, implementation flaws, etc. We implemented a prototype of the CT watcher and conducted a 52-day trial operation and several confirmation experiments involving 8.26M unique certificates of about 6,000 domains. From the results returned by 6 active third-party monitors in the wild, the prototype detected 14 potential design or implementation issues of these monitors, demonstrating its effectiveness in public inspections on third-party monitors and the potential to improve the overall reliability of CT.
Today, smart home platforms are widely used around the world and offer users automation to define their daily routines. However, individual automation rule anomalies and cross-automation threats that exist in different platforms put the smart home in danger. Recent researches focus on detecting these threats of the specific platform and can only cover limited threat plane. To solve these problems, we design a novel system called CP-IoT, which can monitor the execution behavior of the automation and discover the anomalies, as well as hidden risks among them on heterogeneous IoT platforms. Specifically, CP-IoT constructs a centralized, dynamic graph model for portraying the behavior of automation and the state transition. By analyzing two kinds of app pages with different description granularity, CP-IoT extracts the rule execution logic and collects user policy from different platforms. To detect the inconsistent behavior of an automation rule in different platforms, we propose a self-learning method for event fingerprint extraction by clustering the traffic of different platforms collected from the side channel, and an anomaly detection method by checking the rule execution behavior with its specification reflected in the graph model. To detect the cross-rule threats, we formalize each threat type as a symbolic representation and apply the searching algorithm on the graph. We validate the performance of CP-IoT on four platforms. The evaluation shows that CP-IoT can detect anomalies with high accuracy and effectively discover various types of cross-rule threats.
ROS2 is a popular publish/subscribe based middleware that allows developers to build and deploy a wide-variety of distributed robotics applications. Unfortunately, ROS2 offers applications poor control over how their data is consumed by downstream applications. We present Picaros, a decentralized information-flow control (DIFC) system tailored for ROS2. The decentralized and distributed architecture of ROS2 poses new challenges to building a DIFC system that prior work has not addressed. Picaros adopts a novel approach to address these challenges by casting and solving the problem of DIFC within the framework of attribute-based encryption (ABE). Picaros's design embraces the unique nature of the ROS2 platform and carefully avoids any centralized elements. This paper presents the design and implementation of Picaros and reports on our experiments that use Picaros's ABE-based approach for DIFC with ROS2 applications.
Directed Greybox Fuzzing (DGF) is an effective approach designed to strengthen testing vulnerable code areas via predefined target sites. The state-of-the-art DGF techniques redefine and optimize the fitness metric to reach the target sites precisely and quickly. However, optimizations for fitness metrics are mainly based on heuristic algorithms, which usually rely on historical execution information and lack foresight on paths that have not been exercised yet. Thus, those hard-to-execute paths with complex constraints would hinder DGF from reaching the targets, making DGF less efficient. In this paper, we propose DeepGo, a predictive directed greybox fuzzer that can combine historical and predicted information to steer DGF to reach the target site via an optimal path. We first propose the path transition model, which models DGF as a process of reaching the target site through specific path transition sequences. The new seed generated by mutation would cause the path transition, and the path corresponding to the high-reward path transition sequence indicates a high likelihood of reaching the target site through it. Then, to predict the path transitions and the corresponding rewards, we use deep neural networks to construct a Virtual Ensemble Environment (VEE), which gradually imitates the path transition model and predicts the rewards of path transitions that have not been taken yet. To determine the optimal path, we develop a Reinforcement Learning for Fuzzing (RLF) model to generate the transition sequences with the highest sequence rewards. The RLF model can combine historical and predicted path transitions to generate the optimal path transition sequences, along with the policy to guide the mutation strategy of fuzzing. Finally, to exercise the high-reward path transition sequence, we propose the concept of an action group, which comprehensively optimizes the critical steps of fuzzing to realize the optimal path to reach the target efficiently. We evaluated DeepGo on 2 benchmarks consisting of 25 programs with a total of 100 target sites. The experimental results show that DeepGo achieves 3.23×, 1.72×, 1.81×, and 4.83× speedup compared to AFLGo, BEACON, WindRanger, and ParmeSan, respectively in reaching target sites, and 2.61×, 3.32×, 2.43× and 2.53× speedup in exposing known vulnerabilities.
Reverse engineering is essential in malware analysis, vulnerability discovery, etc. Decompilers assist the reverse engineers by lifting the assembly to the high-level programming language, which highly boosts binary comprehension. However, decompilers suffer from problems such as meaningless variable names, redundant variables, and lacking comments describing the purpose of the code. Previous studies have shown promising performance in refining the decompiler output by training the models with huge datasets containing various decompiler outputs. However, even datasets that take much time to construct cover limited binaries in the real world. The performance degrades severely facing the binary migration. In this paper, we present DeGPT, an end-to-end framework aiming to optimize the decompiler output to improve its readability and simplicity and further assist the reverse engineers in understanding the binaries better. The Large Language Model (LLM) can mitigate performance degradation with its extraordinary ability endowed by large model size and training set containing rich multi-modal data. However, its potential is difficult to unlock through one-shot use. Thus, we propose the three-role mechanism, which includes referee (R_ref), advisor (R_adv), and operator (R_ope), to adapt the LLM to our optimization tasks. Specifically, R_ref provides the optimization scheme for the target decompiler output, while R_adv gives the rectification measures based on the scheme, and R_ope inspects whether the optimization changes the original function semantics and concludes the final verdict about whether to accept the optimizations. We evaluate DeGPT on the datasets containing decompiler outputs of various software, such as the practical command line tools, malware, a library for audio processing, and implementations of algorithms. The experimental results show that even on the output of the current top-level decompiler (Ghidra), DeGPT can achieve 24.4% reduction in the cognitive burden of understanding the decompiler outputs and provide comments of which 62.9% can provide practical semantics for the reverse engineers to help the understanding of binaries. Our user surveys also show that the optimizations can significantly simplify the code and add helpful semantic information (variable names and comments), facilitating a quick and accurate understanding of the binary.
The potential misuse of ChatGPT and other Large Language Models (LLMs) has raised concerns regarding the dissemination of false information, plagiarism, academic dishonesty, and fraudulent activities. Consequently, distinguishing between AI-generated and human-generated content has emerged as an intriguing research topic. However, current text detection methods lack precision and are often restricted to specific tasks or domains, making them inadequate for identifying content generated by ChatGPT. In this paper, we propose an effective ChatGPT detector named DEMASQ, which accurately identifies ChatGPT-generated content. Our method addresses two critical factors: (i) the distinct biases in text composition observed in human and machine-generated content and (ii) the alterations made by humans to evade previous detection methods. DEMASQ is an energy-based detection model that incorporates novel aspects, such as (i) optimization inspired by the Doppler effect to capture the interdependence between input text embeddings and output labels, and (ii) the use of explainable AI techniques to generate diverse perturbations. To evaluate our detector, we create a benchmark dataset comprising a mixture of prompts from both ChatGPT and humans, encompassing domains such as medical, open Q&A, finance, wiki, and Reddit. Our evaluation demonstrates that DEMASQ achieves high accuracy in identifying content generated by ChatGPT.
Nowadays, it is common to release audio content to the public, for social sharing or commercial purposes. However, with the rise of voice cloning technology, attackers have the potential to easily impersonate a specific person by utilizing his publicly released audio without any permission. Therefore, it becomes significant to detect any potential misuse of the released audio content and protect its timbre from being impersonated. To this end, we introduce a novel concept, "Timbre Watermarking", which embeds watermark information into the target individual's speech, eventually defeating the voice cloning attacks. However, there are two challenges: 1) robustness: the attacker can remove the watermark with common speech preprocessing before launching voice cloning attacks; 2) generalization: there are a variety of voice cloning approaches for the attacker to choose, making it hard to build a general defense against all of them. To address these challenges, we design an end-to-end voice cloning-resistant detection framework. The core idea of our solution is to embed the watermark into the frequency domain, which is inherently robust against common data processing methods. A repeated embedding strategy is adopted to further enhance the robustness. To acquire generalization across different voice cloning attacks, we modulate their shared process and integrate it into our framework as a distortion layer. Experiments demonstrate that the proposed timbre watermarking can defend against different voice cloning attacks, exhibit strong resistance against various adaptive attacks (e.g., reconstruction-based removal attacks, watermark overwriting attacks), and achieve practicality in real-world services such as PaddleSpeech, Voice-Cloning-App, and so-vits-svc. In addition, ablation studies are also conducted to verify the effectiveness of our design. Some audio samples are available at https://timbrewatermarking.github.io/samples.