Total: 80
Targeted deanonymization attacks let a malicious website discover whether a website visitor bears a certain public identifier, such as an email address or a Twitter handle. These attacks were previously considered to rely on several assumptions, limiting their practical impact. In this work, we challenge these assumptions and show the attack surface for deanonymization attacks is drastically larger than previously considered. We achieve this by using the cache side channel for our attack, instead of relying on cross-site leaks. This makes our attack oblivious to recently proposed software-based isolation mechanisms, including cross-origin resource policies (CORP), cross-origin opener policies (COOP) and SameSite cookie attribute. We evaluate our attacks on multiple hardware microarchitectures, multiple operating systems and multiple browser versions, including the highly-secure Tor Browser, and demonstrate practical targeted deanonymization attacks on major sites, including Google, Twitter, LinkedIn, TikTok, Facebook, Instagram and Reddit. Our attack runs in less than 3 seconds in most cases, and can be scaled to target an exponentially large amount of users. To stop these attacks, we present a full-featured defense deployed as a browser extension. To minimize the risk to vulnerable individuals, our defense is already available on the Chrome and Firefox app stores. We have also responsibly disclosed our findings to multiple tech vendors, as well as to the Electronic Frontier Foundation. Finally, we provide guidance to websites and browser vendors, as well as to users who cannot install the extension.
The needs of marginalised groups like migrant domestic workers (MDWs) are often ignored in digital privacy and security research. If considered, MDWs are treated as 'bystanders' or even as threats rather than as targets of surveillance and legitimate security subjects in their own right. Using participatory threat modelling (PTM) as a method of incorporating marginalised populations' experiences, we designed and conducted five workshops with MDWs (n=32) in the UK to identify threats to their privacy and security. We found that MDWs named government surveillance, scams and harassment, and employer monitoring (in this order) as the primary threats to their privacy and security. We also examined the methods MDWs used to stay safe online, such as configuring the privacy settings of their online accounts and creating on- and offline community support networks. Based on our findings, we developed and disseminated a digital privacy and security guide with links to further resources that MDWs can refer to. We conclude by arguing that security research must consider broader social structures like gendered work and racialised border policy that foster insecurity in the lives of MDWs. We also present the key lessons of our work, including considering data sharing from the perspective of stakeholders who do not own technology devices but are affected by them, and reflecting on how security research can stop enabling harmful forms of surveillance.
Deep learning systems are known to be vulnerable to adversarial examples. In particular, query-based black-box attacks do not require knowledge of the deep learning model, but can compute adversarial examples over the network by submitting queries and inspecting returns. Recent work largely improves the efficiency of those attacks, demonstrating their practicality on today's ML-as-a-service platforms. We propose Blacklight, a new defense against query-based black-box adversarial attacks. Blacklight is driven by a fundamental insight: to compute adversarial examples, these attacks perform iterative optimization over the network, producing queries highly similar in the input space. Thus Blacklight detects query-based black-box attacks by detecting highly similar queries, using an efficient similarity engine operating on probabilistic content fingerprints. We evaluate Blacklight against eight state-of-the-art attacks, across a variety of models and image classification tasks. Blacklight identifies them all, often after only a handful of queries. By rejecting all detected queries, Blacklight prevents any attack from completing, even when persistent attackers continue to submit queries after banned accounts or rejected queries. Blacklight is also robust against several powerful countermeasures, including an optimal black-box attack that approximates white-box attacks in efficiency. Finally, we illustrate how Blacklight generalizes to other domains like text classification.
Route hijacking is one of the most severe security problems in today's Internet, and route origin hijacking is the most common. While origin hijacking detection systems are already available, they suffer from tremendous pressures brought by frequent legitimate Multiple origin ASes (MOAS) conflicts. They detect MOAS conflicts on the control plane and then identify origin hijackings by data-plane probing or even manual verification. However, legitimate changes in prefix ownership can also cause MOAS conflicts, which are the majority of MOAS conflicts daily. Massive legitimate MOAS conflicts consume many resources for probing and identification, resulting in high verification costs and high verification latency in practice. In this paper, we propose a new origin hijacking system Themis to accelerate the detection of origin hijacking. Based on the ground truth dataset we built, we analyze the characteristics of different MOAS conflicts and train a classifier to filter out legitimate MOAS conflicts on the control plane. The accuracy and recall of the MOAS classifier are 95.49% and 99.20%, respectively. Using the MOAS classifier, Themis reduces 56.69% of verification costs than Argus, the state-of-the-art, and significantly accelerates the detection when many concurrent MOAS conflicts occur. The overall accuracy of Themis is almost the same as Argus.
Adversarial attacks can fool deep learning models by imposing imperceptible perturbations onto natural examples, which have provoked concerns in various security-sensitive applications. Among them, decision-based black-box attacks are practical yet more challenging, where the adversary can only acquire the final classification labels by querying the target model without access to the model's details. Under this setting, existing works usually rely on heuristics and exhibit unsatisfactory performance in terms of query efficiency and attack success rate. To better understand the rationality of these heuristics and further improve over existing methods, we propose AutoDA to automatically discover decision-based iterative adversarial attack algorithms. In our approach, we construct a generic search space of attack algorithms and develop an efficient search algorithm to explore this space. Although we adopt a small and fast model to efficiently evaluate and discover qualified attack algorithms during the search, extensive experiments demonstrate that the discovered algorithms are simple yet query-efficient when attacking larger models on the CIFAR-10 and ImageNet datasets. They achieve comparable performance with the human-designed state-of-the-art decision-based iterative attack methods consistently.
Symbolic security protocol verifiers have reached a high degree of automation and maturity. Today, experts can model real-world protocols, but this often requires model-specific encodings and deep insight into the strengths and weaknesses of each of those tools. With SAPIC+, we introduce a protocol verification platform that lifts this burden and permits choosing the right tool for the job, at any development stage. We build on the existing compiler from SAPIC to TAMARIN, and extend it with automated translations from SAPIC+ to PROVERIF and DEEPSEC, as well as powerful, protocol-independent optimizations of the existing translation. We prove each part of these translations sound. A user can thus, with a single SAPIC+ file, verify reachability and equivalence properties on the specified protocol, either using PROVERIF, TAMARIN or DEEPSEC. Moreover, the soundness of the translation allows to directly assume results proven by another tool which allows to exploit the respective strengths of each tool. We demonstrate our approach by analyzing various existing models. This includes a large case study of the 5G authentication protocols, previously analyzed in TAMARIN. Encoding this model in SAPIC+ we demonstrate the effectiveness of our approach. Moreover, we study four new case studies: the LAKE-EDHOC and the Privacy-Pass protocols, both under standardization, the SSH protocol with the agent-forwarding feature, and the recent KEMTLS protocol, a post-quantum version of the main TLS key exchange.
Studying developers is an important aspect of usable security and privacy research. In particular, studying security development challenges such as the usability of security APIs, the secure use of information sources during development or the effectiveness of IDE security plugins raised interest in recent years. However, recruiting skilled participants with software development experience is particularly challenging, and it is often not clear what security researchers can expect from certain participant samples, which can make research results hard to compare and interpret. Hence, in this work, we study for the first time opportunities and challenges of different platforms to recruit participants with software development experience for security development studies. First, we identify popular recruitment platforms in 59 papers. Then, we conduct a comparative online study with 706 participants based on self-reported software development experience across six recruitment platforms. Using an online questionnaire, we investigate participants' programming and security experiences, skills and knowledge. We find that participants across all samples report rich general software development and security experience, skills, and knowledge. Based on our results, we recommend developer recruitment from Upwork for practical coding studies and Amazon MTurk along with a pre-screening survey to reduce additional noise for larger studies. Both of these, along with Freelancer, are also recommended for security studies. We conclude the paper by discussing the impact of our results on future security development studies.
Modern disassembly tools often rely on empirical evaluations to validate their performance and discover their limitations, thus promoting long-term evolvement. To support the empirical evaluation, a foundation is the right approach to collect the ground truth knowledge. However, there has been no unanimous agreement on the approach we should use. Most users pick an approach based on their experience or will, regardless of the properties that the approach presents. In this paper, we perform a study on the approaches to building the ground truth for binary disassembly, aiming to shed light on the right way for the future. We first provide a taxonomy of the approaches used by past research, which unveils five major mechanisms behind those approaches. Following the taxonomy, we summarize the properties of the five mechanisms from two perspectives: (i) the coverage and precision of the ground truth produced by the mechanisms and (ii) the applicable scope of the mechanisms (e.g., what disassembly tasks and what types of binaries are supported). The summarization, accompanied by quantitative evaluations, illustrates that many mechanisms are ill-suited to support the generation of disassembly ground truth. The mechanism best serving today's need is to trace the compiling process of the target binaries to collect the ground truth information. Observing that the existing tool to trace the compiling process can still miss ground truth results and can only handle x86/x64 binaries, we extend the tool to avoid overlooking those results and support ARM32/AArch64/MIPS32/MIPS64 binaries. We envision that our extension will make the tool a better foundation to enable universal, standard ground truth for binary disassembly.
The monolithic programming model has been favored for high compatibility and easing the programming for SGX enclaves, i.e., running the secure code with all dependent libraries or even library OSes (LibOSes). Yet, it inevitably bloats the trusted computing base (TCB) and thus deviates from the goal of high security. Introducing fine-grained isolation can effectively mitigate TCB bloating while existing solutions face performance issues. We observe that the off-the-shelf Intel MPK is a perfect match for efficient intra-enclave isolation. Nonetheless, the trust models between MPK and SGX are incompatible by design. We hence propose LIGHTENCLAVE, which embraces non-intrusive extensions on existing SGX hardware to incorporate MPK securely and allows multiple light-enclaves isolated within one enclave. Experiments show that LIGHTENCLAVE incurs up to 4% overhead when separating secret SSL keys for server applications and can significantly improve the performance of Graphene-SGX and Occlum by reducing the communication and runtime overhead, respectively.
In adversarial machine learning, new defenses against attacks on deep learning systems are routinely broken soon after their release by more powerful attacks. In this context, forensic tools can offer a valuable complement to existing defenses, by tracing back a successful attack to its root cause, and offering a path forward for mitigation to prevent similar attacks in the future. In this paper, we describe our efforts in developing a forensic traceback tool for poison attacks on deep neural networks. We propose a novel iterative clustering and pruning solution that trims "innocent" training samples, until all that remains is the set of poisoned data responsible for the attack. Our method clusters training samples based on their impact on model parameters, then uses an efficient data unlearning method to prune innocent clusters. We empirically demonstrate the efficacy of our system on three types of dirty-label (backdoor) poison attacks and three types of clean-label poison attacks, across domains of computer vision and malware classification. Our system achieves over 98.4% precision and 96.8% recall across all attacks. We also show that our system is robust against four anti-forensics measures specifically designed to attack it.
We quantitatively investigated the current state of Password Manager (PM) usage and general password habits at a large, private university in the United States. Building on prior qualitative findings from SOUPS 2019, we survey n=277 faculty, staff, and students, finding that 77% of our participants already use PMs, but users of third-party PMs, as opposed to browser-based PMs, were significantly less likely to reuse their passwords across accounts. The largest factor encouraging PM adoption is perceived ease-of-use, indicating that communication and institutional campaigns should focus more on usability factors. Additionally, our work indicates the need for design improvements for browser-based PMs to encourage less password reuse as they are more widely adopted.
Adversaries can exploit inter-domain routing vulnerabilities to intercept communication and compromise the security of critical Internet applications. Meanwhile the deployment of secure routing solutions such as Border Gateway Protocol Security (BGPsec) and Scalability, Control and Isolation On Next-generation networks (SCION) are still limited. How can we leverage emerging secure routing backbones and extend their security properties to the broader Internet? We design and deploy an architecture to bootstrap secure routing. Our key insight is to abstract the secure routing backbone as a virtual Autonomous System (AS), called Secure Backbone AS (SBAS). While SBAS appears as one AS to the Internet, it is a federated network where routes are exchanged between participants using a secure backbone. SBAS makes BGP announcements for its customers' IP prefixes at multiple locations (referred to as Points of Presence or PoPs) allowing traffic from non-participating hosts to be routed to a nearby SBAS PoP (where it is then routed over the secure backbone to the true prefix owner). In this manner, we are the first to integrate a federated secure non-BGP routing backbone with the BGP-speaking Internet. We present a real-world deployment of our architecture that uses SCIONLab to emulate the secure backbone and the PEERING framework to make BGP announcements to the Internet. A combination of real-world attacks and Internet-scale simulations shows that SBAS substantially reduces the threat of routing attacks. Finally, we survey network operators to better understand optimal governance and incentive models.
Universal cross-site scripting (UXSS) is a browser vulnerability, making a vulnerable browser execute an attacker's script on any web pages loaded by the browser. UXSS is considered a far more severe vulnerability than well-studied cross-site scripting (XSS). This is because the impact of UXSS is not limited to a web application, but it impacts each and every web application as long as a victim user runs a vulnerable browser. We find that UXSS vulnerabilities are difficult to find, especially through fuzzing, for the following two reasons. First, it is challenging to detect UXSS because it is a semantic vulnerability. In order to detect UXSS, one needs to understand the complex interaction semantics between web pages. Second, it is difficult to generate HTML inputs that trigger UXSS since one needs to drive the browser to perform complex interactions and navigations. This paper proposes FuzzOrigin, a browser fuzzer designed to detect UXSS vulnerabilities. FuzzOrigin addresses the above two challenges by (i) designing an origin sanitizer with a static origin tagging mechanism and (ii) prioritizing origin-update operations through generating chained-navigation operations handling dedicated events. We implemented FuzzOrigin, which works with most modern browsers, including Chrome, Firefox, Edge, and Safari. During the evaluation, FuzzOrigin discovered four previously unknown UXSS vulnerabilities, one in Chrome and three in Firefox, all of which have been confirmed by the vendors. FuzzOrigin is responsible for finding one out of two UXSS vulnerabilities in Chrome reported in 2021 and all three in Firefox, highlighting its strong effectiveness in finding new UXSS vulnerabilities.
Peripheral hardware in modern computers is typically assumed to be secure and not malicious, and device drivers are implemented in a way that trusts inputs from hardware. However, recent vulnerabilities such as Broadpwn have demonstrated that attackers can exploit hosts through vulnerable peripherals, highlighting the importance of securing the OS-peripheral boundary. In this paper, we propose a hardware-free concolic-augmented fuzzer targeting WiFi and Ethernet drivers, and a technique for generating high-quality initial seeds, which we call golden seeds, that allow fuzzing to bypass difficult code constructs during driver initialization. Compared to prior work using symbolic execution or greybox fuzzing, Drifuzz is more successful at automatically finding inputs that allow network interfaces to be fully initialized, and improves fuzzing coverage by 214% (3.1×) in WiFi drivers and 60% (1.6×) for Ethernet drivers. During our experiments with fourteen PCI and USB network drivers, we find twelve previously unknown bugs, two of which were assigned CVEs.
ARM is becoming more popular in desktops and data centers, opening a new realm in terms of security attacks against ARM. ARM has released Pointer Authentication, a new hardware security feature that is intended to ensure pointer integrity with cryptographic primitives. In this paper, we utilize Pointer Authentication (PA) to build a novel scheme to completely prevent any misuse of security-sensitive pointers. We propose PACTIGHT to tightly seal these pointers. PACTIGHT utilizes a strong and unique modifier that addresses the current issues with the state-of-the-art PA defense mechanisms. We implement four defenses based on the PACTIGHT mechanism. Our security and performance evaluation results show that PACTIGHT defenses are more efficient and secure. Using real PA instructions, we evaluated PACTIGHT on 30 different applications, including NGINX web server, with an average performance overhead of 4.07% even when enforcing our strongest defense. PACTIGHT demonstrates its effectiveness and efficiency with real PA instructions on real hardware.
Public-key authentication in SSH reveals more information about the participants' keys than is necessary. (1) The server can learn a client's entire set of public keys, even keys generated for other servers. (2) The server learns exactly which key the client uses to authenticate, and can further prove this fact to a third party. (3) A client can learn whether the server recognizes public keys belonging to other users. Each of these problems lead to tangible privacy violations for SSH users. In this work we introduce a new public-key authentication method for SSH that reveals essentially the minimum possible amount of information. With our new method, the server learns only whether the client knows the private key for some authorized public key. If multiple keys are authorized, the server does not learn which one the client used. The client cannot learn whether the server recognizes public keys belonging to other users. Unlike traditional SSH authentication, our method is fully deniable. Our method supports existing SSH keypairs of all standard flavors—RSA, ECDSA, EdDSA. It does not require users to generate new key material. As in traditional SSH authentication, clients and servers can use a mixture of different key flavors in a single authentication session. We integrated our new authentication method into OpenSSH, and found it to be practical and scalable. For a typical client and server with at most 10 ECDSA/EdDSA keys each, our protocol requires 9 kB of communication and 12.4 ms of latency. Even for a client with 20 keys and server with 100 keys, our protocol requires only 12 kB of communication and 26.7 ms of latency.
Section 702 of the Foreign Intelligence Surveillance Act authorizes U.S. intelligence agencies to intercept communications content without obtaining a warrant. While Section 702 requires targeting foreigners abroad for intelligence purposes, agencies "incidentally" collect communications to or from Americans and can search that data for purposes beyond intelligence gathering. For over a decade, members of Congress and civil society organizations have called on the U.S. Intelligence Community (IC) to estimate the scale of incidental collection. Senior intelligence officials have acknowledged the value of quantitative transparency for incidental collection, but the IC has not identified a satisfactory estimation method that respects individual privacy, protects intelligence sources and methods, and imposes minimal burden on IC resources. In this work, we propose a novel approach to estimating incidental collection using secure multiparty computation (MPC). The IC possesses records about the parties to intercepted communications, and communications services possess country-level location for users. By combining these datasets with MPC, it is possible to generate an automated aggregate estimate of incidental collection that maintains confidentiality for intercepted communications and user locations. We formalize our proposal as a new variant of private set intersection, which we term multiparty private set intersection with union and sum (MPSIU-Sum). We then design and evaluate an efficient MPSIU-Sum protocol, based on elliptic curve cryptography and partially homomorphic encryption. Our protocol performs well at the large scale necessary for estimating incidental collection in Section 702 surveillance.
Effective query recovery attacks against Searchable Symmetric Encryption (SSE) schemes typically rely on auxiliary ground-truth information about the queries or dataset. Query recovery is also possible under the weaker statistical auxiliary information assumption, although statistical-based attacks achieve lower accuracy and are not considered a serious threat. In this work we present IHOP, a statistical-based query recovery attack that formulates query recovery as a quadratic optimization problem and reaches a solution by iterating over linear assignment problems. We perform an extensive evaluation with five real datasets, and show that IHOP outperforms all other statistical-based query recovery attacks under different parameter and leakage configurations, including the case where the client uses some access-pattern obfuscation defenses. In some cases, our attack achieves almost perfect query recovery accuracy. Finally, we use IHOP in a frequency-only leakage setting where the client's queries are correlated, and show that our attack can exploit query dependencies even when PANCAKE, a recent frequency-hiding defense by Grubbs et al., is applied. Our findings indicate that statistical query recovery attacks pose a severe threat to privacy-preserving SSE schemes.
Payment channel networks (PCNs) provide a faster and cheaper alternative to transactions recorded on the blockchain. Clients can trustlessly establish payment channels with relays by locking coins and then send signed payments that shift coin balances over the network's channels. Although payments are never published, anyone can track a client's payment by monitoring changes in coin balances over the network's channels. We present Twilight, the first PCN that provides a rigorous differential privacy guarantee to its users. Relays in Twilight run a noisy payment processing mechanism that hides the payments they carry. This mechanism increases the relay's cost, so Twilight combats selfish relays that wish to avoid it, using a trusted execution environment (TEE) that ensures they follow its protocol. The TEE does not store the channel's state, which minimizes the trusted computing base. Crucially, Twilight ensures that even if a relay breaks the TEE's security, it cannot break the integrity of the PCN. We analyze Twilight in terms of privacy and cost and study the trade-off between them. We implement Twilight using Intel's SGX framework and evaluate its performance using relays deployed on two continents. We show that a route consisting of 4 relays handles 820 payments/sec.
How can we orchestrate an one-off sharing of informative data about individuals, while bounding the risk of disclosing sensitive information to an adversary who has access to the global distribution of such information and to personal identifiers? Despite intensive efforts, current privacy protection techniques fall short of this objective. Differential privacy provides strong guarantees regarding the privacy risk incurred by one's participation in the data at the cost of high information loss and is vulnerable to learning-based attacks exploiting correlations among data. Syntactic anonymization bounds the risk on specific sensitive information incurred by data publication, yet typically resorts to a superfluous clustering of individuals into groups that forfeits data utility. In this paper, we develop algorithms for disclosure control that abide to sensitive-information-oriented syntactic privacy guarantees and gain up to 77% in utility against current methods. We achieve this feat by recasting data heterogeneously, via bipartite matching, rather than homogeneously via clustering. We show that our methods resist adversaries who know the employed algorithm and its parameters. Our experimental study featuring synthetic and real data, as well as real learning and data analysis tasks, shows that these methods enhance data utility with a runtime overhead that is small and reducible by data partitioning, while the β-likeness guarantee with heterogeneous generalization staunchly resists machine-learning-based attacks, hence offers practical value.
Hardware flaws are permanent and potent: hardware cannot be patched once fabricated, and any flaws may undermine even formally verified software executing on top. Consequently, verification time dominates implementation time. The gold standard in hardware Design Verification (DV) is dynamic random testing, due to its scalability to large designs. However, given its undirected nature, this technique is inefficient. Instead of making incremental improvements to existing dynamic hardware verification approaches, we leverage the observation that existing software fuzzers already provide such a solution, and hence adapt them for hardware verification. Specifically, we translate RTL hardware to a software model and fuzz that model directly. The central challenge we address is how to mitigate the differences between the hardware and software execution models. This includes: 1) how to represent test cases, 2) what is the hardware equivalent of a crash, 3) what is an appropriate coverage metric, and 4) how to create a general-purpose fuzzing harness for hardware. To evaluate our approach, we design, implement, and open-source a Hardware Fuzzing Pipeline that enables fuzzing hardware at scale, using only open-source tools. Using our pipeline, we fuzz five IP blocks from Google's OpenTitan Root-of-Trust chip, four SiFive TileLink peripherals, three RISC-V CPUs, and an FFT accelerator. Our experiments reveal a two orders-of-magnitude reduction in run time to achieve similar Finite State Machine coverage over traditional dynamic verification schemes, and 26.70% better HDL line coverage than prior work. Moreover, with our bus-centric harness, we achieve over 83% HDL line coverage in four of the five OpenTitan IPs we study—without any initial seeds—and are able to detect all bugs (four synthetic from Hack@DAC and one real) implanted across all five OpenTitan IPs we study, with less than 10 hours of fuzzing.
Transfer learning has become a common solution to address training data scarcity in practice. It trains a specified student model by reusing or fine-tuning early layers of a well-trained teacher model that is usually publicly available. However, besides utility improvement, the transferred public knowledge also brings potential threats to model confidentiality, and even further raises other security and privacy issues. In this paper, we present the first comprehensive investigation of the teacher model exposure threat in the transfer learning context, aiming to gain a deeper insight into the tension between public knowledge and model confidentiality. To this end, we propose a teacher model fingerprinting attack to infer the origin of a student model, i.e., the teacher model it transfers from. Specifically, we propose a novel optimization-based method to carefully generate queries to probe the student model to realize our attack. Unlike existing model reverse engineering approaches, our proposed fingerprinting method neither relies on fine-grained model outputs, e.g., posteriors, nor auxiliary information of the model architecture or training dataset. We systematically evaluate the effectiveness of our proposed attack. The empirical results demonstrate that our attack can accurately identify the model origin with few probing queries. Moreover, we show that the proposed attack can serve as a stepping stone to facilitating other attacks against machine learning models, such as model stealing.
Secure two-party protocols that compute intersection-related statistics have attracted much attention from the industry. These protocols enable two organizations to jointly compute a function (e.g., count and sum) over the intersection of their sets without explicitly revealing this intersection. However, most of such protocols will reveal the intersection size of the two sets in the end. In this work, we are interested in how well an attacker can leverage the revealed intersection sizes to infer some elements' membership of one organization's set. Even disclosing an element's membership of one organization's set to the other organization may violate privacy regulations (e.g., GDPR) since such an element is usually used to identify a person between two organizations. We are the first to study this set membership leakage in intersection-size-revealing protocols. We propose two attacks, namely, baseline attack and feature-aware attack, to evaluate this leakage in realistic scenarios. In particular, our feature-aware attack exploits the realistic set bias that elements with specific features are more likely to be the members of one organization's set. The results show that our two attacks can infer 2.0 ∼ 72.7 set members on average in three realistic scenarios. If the set bias is not weak, the feature-aware attack will outperform the baseline one. For example, in COVID-19 contact tracing, the feature-aware attack can find 25.9 tokens of infected patients in 135 protocol invocations, 1.5 × more than the baseline attack. We discuss how such results may cause negative real-world impacts and propose possible defenses against our attacks.
ICMP redirect is a mechanism that allows an end host to dynamically update its routing decisions for particular destinations. Previous studies show that ICMP redirect may be exploited by attackers to manipulate the routing of victim traffic. However, it is widely believed that ICMP redirect attacks are not a real-world threat since they can only occur under specific network topologies (e.g., LAN). In this paper, we conduct a systematic study on the legitimacy check mechanism of ICMP and uncover a fundamental gap between the check mechanism and stateless protocols, resulting in a wide range of vulnerabilities. In particular, we find that off-path attackers can utilize a suite of stateless protocols (e.g., UDP, ICMP, GRE, IPIP and SIT) to easily craft evasive ICMP error messages, thus revitalizing ICMP redirect attacks to cause serious damage in the real world, particularly, on the wide-area network. First, we show that off-path attackers can conduct a stealthy DoS attack by tricking various public servers on the Internet into mis-redirecting their traffic into black holes with a single forged ICMP redirect message. For example, we reveal that more than 43K popular websites on the Internet are vulnerable to this DoS attack. In addition, we identify 54.47K open DNS resolvers and 186 Tor nodes on the Internet are vulnerable as well. Second, we show that, by leveraging ICMP redirect attacks against NATed networks, off-path attackers in the same NATed network can perform a man-in-the-middle (MITM) attack to intercept the victim traffic. Finally, we develop countermeasures to throttle the attacks.
Smart home devices transmit highly sensitive usage information to servers owned by vendors or third-parties as part of their core functionality. Hence, it is necessary to provide users with the context in which their device data is collected and shared, to enable them to weigh the benefits of deploying smart home technology against the resulting loss of privacy. As privacy policies are generally expected to precisely convey this information, we perform a systematic and data-driven analysis of the current state of smart home privacy policies, with a particular focus on three key questions: (1) how hard privacy policies are for consumers to obtain, (2) how existing policies describe the collection and sharing of device data, and (3) how accurate these descriptions are when compared to information derived from alternate sources. Our analysis of 596 smart home vendors, affecting 2, 442 smart home devices yields 17 findings that impact millions of users, demonstrate gaps in existing smart home privacy policies, as well as challenges and opportunities for automated analysis.