arXiv subjects

Thomas Ristenpart

Publications and source records attributed to Thomas Ristenpart.

At least 19 recordsLinked to original sources

arXiv2025

Transcript Franking for Encrypted Messaging

Message franking is an indispensable abuse mitigation tool for end-to-end encrypted (E2EE) messaging platforms. With it, users who receive harmful content can securely report that content to platform moderators. However, while real-world deployments of reporting require the disclosure of multiple messages, existing treatments of message franking only consider the report of a single message. As a result, there is a gap between the security goals achieved by constructions and those needed in practice. Our work introduces transcript franking, a new type of protocol that allows reporting subsets of conversations such that moderators can cryptographically verify message causality and contents. We define syntax, semantics, and security for transcript franking in two-party and group messaging. We then present efficient constructions for transcript franking and prove their security. Looking toward deployment considerations, we provide detailed discussion of how real-world messaging systems can incorporate our protocols.

cs.CR↗

arXiv2025

Rerouting LLM Routers

LLM routers aim to balance quality and cost of generation by classifying queries and routing them to a cheaper or more expensive LLM depending on their complexity. Routers represent one type of what we call LLM control planes: systems that orchestrate use of one or more LLMs. In this paper, we investigate routers' adversarial robustness. We first define LLM control plane integrity, i.e., robustness of LLM orchestration to adversarial inputs, as a distinct problem in AI safety. Next, we demonstrate that an adversary can generate query-independent token sequences we call ``confounder gadgets'' that, when added to any query, cause LLM routers to send the query to a strong LLM. Our quantitative evaluation shows that this attack is successful both in white-box and black-box settings against a variety of open-source and commercial routers, and that confounding queries do not affect the quality of LLM responses. Finally, we demonstrate that gadgets can be effective while maintaining low perplexity, thus perplexity-based filtering is not an effective defense. We finish by investigating alternative defenses.

cs.CR↗

arXiv2024

Mitigating Trauma in Qualitative Research Infrastructure: Roles for Machine Assistance and Trauma-Informed Design

Researchers increasingly look to understand experiences of pain, harm, and marginalization via qualitative analysis. Such work is needed to understand and address social ills, but poses risks to researchers' well-being: sifting through volumes of data on painful human experiences risks incurring traumatic exposure in the researcher. In this paper, we explore how the principles of trauma-informed computing (TIC) can be applied to reimagine healthier tools and workflows for qualitative analysis. We apply TIC to create a design provocation called TIQA, a system for qualitative coding that leverages language modeling, semantic search, and recommendation systems to measure and mitigate an analyst's exposure to concepts they find traumatic. Through a formative study of TIQA with 15 participants, we illuminate the complexities of enacting TIC in qualitative knowledge infrastructure, and potential roles for machine assistance in mitigating researchers' trauma. To assist scholars in translating the high-level principles of TIC into sociotechnical system design, we argue for: (a) a conceptual shift from safety as exposure reduction towards safety as enablement; and (b) renewed attention to evaluating the trauma-informedness of design processes, in tandem with the outcomes of designed objects on users' well-being.

cs.HC↗

arXiv2024

Injection Attacks Against End-to-End Encrypted Applications

We explore an emerging threat model for end-to-end (E2E) encrypted applications: an adversary sends chosen messages to a target client, thereby "injecting" adversarial content into the application state. Such state is subsequently encrypted and synchronized to an adversarially-visible storage. By observing the lengths of the resulting cloud-stored ciphertexts, the attacker backs out confidential information. We investigate this injection threat model in the context of state-of-the-art encrypted messaging applications that support E2E encrypted backups. We show proof-of-concept attacks that can recover information about E2E encrypted messages or attachments sent via WhatsApp, assuming the ability to compromise the target user's Google or Apple account (which gives access to encrypted backups). We also show weaknesses in Signal's encrypted backup design that would allow injection attacks to infer metadata including a target user's number of contacts and conversations, should the adversary somehow obtain access to the user's encrypted Signal backup. While we do not believe our results should be of immediate concern for users of these messaging applications, our results do suggest that more work is needed to build tools that enjoy strong E2E security guarantees.

cs.CR↗

arXiv2024

Exploiting Leakage in Password Managers via Injection Attacks

This work explores injection attacks against password managers. In this setting, the adversary (only) controls their own application client, which they use to "inject" chosen payloads to a victim's client via, for example, sharing credentials with them. The injections are interleaved with adversarial observations of some form of protected state (such as encrypted vault exports or the network traffic received by the application servers), from which the adversary backs out confidential information. We uncover a series of general design patterns in popular password managers that lead to vulnerabilities allowing an adversary to efficiently recover passwords, URLs, usernames, and attachments. We develop general attack templates to exploit these design patterns and experimentally showcase their practical efficacy via analysis of ten distinct password manager applications. We disclosed our findings to these vendors, many of which deployed mitigations.

cs.CR↗

arXiv2024

Private Hierarchical Governance for Encrypted Messaging

The increasing harms caused by hate, harassment, and other forms of abuse online have motivated major platforms to explore hierarchical governance. The idea is to allow communities to have designated members take on moderation and leadership duties; meanwhile, members can still escalate issues to the platform. But these promising approaches have only been explored in plaintext settings where community content is public to the platform. It is unclear how one can realize hierarchical governance in the huge and increasing number of online communities that utilize end-to-end encrypted (E2EE) messaging for privacy. We propose private hierarchical governance systems. These should enable similar levels of community governance as in plaintext settings, while maintaining cryptographic privacy of content and governance actions not reported to the platform. We design the first such system, taking a layered approach that adds governance logic on top of an encrypted messaging protocol; we show how an extension to the message layer security (MLS) protocol suffices for achieving a rich set of governance policies. Our approach allows developers to rapidly prototype new governance features, taking inspiration from a plaintext system called PolicyKit. We build a prototype E2EE messaging system called MlsGov that supports content-based community and platform moderation, elections of community moderators, votes to remove abusive users, and more.

cs.CR↗

arXiv2023

SoK: Safer Digital-Safety Research Involving At-Risk Users

Research involving at-risk users -- that is, users who are more likely to experience a digital attack or to be disproportionately affected when harm from such an attack occurs -- can pose significant safety challenges to both users and researchers. Nevertheless, pursuing research in computer security and privacy is crucial to understanding how to meet the digital-safety needs of at-risk users and to design safer technology for all. To standardize and bolster safer research involving such users, we offer an analysis of 196 academic works to elicit 14 research risks and 36 safety practices used by a growing community of researchers. We pair this inconsistent set of reported safety practices with oral histories from 12 domain experts to contribute scaffolded and consolidated pragmatic guidance that researchers can use to plan, execute, and share safer digital-safety research involving at-risk users. We conclude by suggesting areas for future research regarding the reporting, study, and funding of at-risk user research

cs.CY↗

arXiv2022

Characterizing Alternative Monetization Strategies on YouTube

One of the key emerging roles of the YouTube platform is providing creators the ability to generate revenue from their content and interactions. Alongside tools provided directly by the platform, such as revenue-sharing from advertising, creators co-opt the platform to use a variety of off-platform monetization opportunities. In this work, we focus on studying and characterizing these alternative monetization strategies. Leveraging a large longitudinal YouTube dataset of popular creators, we develop a taxonomy of alternative monetization strategies and a simple methodology to detect their usage automatically. We then proceed to characterize the adoption of these strategies. First, we find that the use of external monetization is expansive and increasingly prevalent, used in 18% of all videos, with 61% of channels using one such strategy at least once. Second, we show that the adoption of these strategies varies substantially among channels of different kinds and popularity, and that channels that establish these alternative revenue streams often become more productive on the platform. Lastly, we investigate how potentially problematic channels -- those that produce Alt-lite, Alt-right, and Manosphere content -- leverage alternative monetization strategies, finding that they employ a more diverse set of such strategies significantly more often than a carefully chosen comparison set of channels. This finding complicates YouTube's role as a gatekeeper, since the practice of excluding policy-violating content from its native on-platform monetization may not be effective. Overall, this work provides an important step toward broadening the understanding of the monetary incentives behind content creation on YouTube.

cs.CY↗

arXiv2022

Might I Get Pwned: A Second Generation Compromised Credential Checking Service

Credential stuffing attacks use stolen passwords to log into victim accounts. To defend against these attacks, recently deployed compromised credential checking (C3) services provide APIs that help users and companies check whether a username, password pair is exposed. These services however only check if the exact password is leaked, and therefore do not mitigate credential tweaking attacks - attempts to compromise a user account with variants of a user's leaked passwords. Recent work has shown credential tweaking attacks can compromise accounts quite effectively even when the credential stuffing countermeasures are in place. We initiate work on C3 services that protect users from credential tweaking attacks. The core underlying challenge is how to identify passwords that are similar to their leaked passwords while preserving honest clients' privacy and also preventing malicious clients from extracting breach data from the service. We formalize the problem and explore ways to measure password similarity that balance efficacy, performance, and security. Based on this study, we design "Might I Get Pwned" (MIGP), a new kind of breach alerting service. Our simulations show that MIGP reduces the efficacy of state-of-the-art 1000-guess credential tweaking attacks by 94%. MIGP preserves user privacy and limits potential exposure of sensitive breach entries. We show that the protocol is fast, with response time close to existing C3 services. We worked with Cloudflare to deploy MIGP in practice.

cs.CR↗

arXiv2021

Increasing Adversarial Uncertainty to Scale Private Similarity Testing

Social media and other platforms rely on automated detection of abusive content to help combat disinformation, harassment, and abuse. One common approach is to check user content for similarity against a server-side database of problematic items. However, this method fundamentally endangers user privacy. Instead, we target client-side detection, notifying only the users when such matches occur to warn them against abusive content. Our solution is based on privacy-preserving similarity testing. Existing approaches rely on expensive cryptographic protocols that do not scale well to large databases and may sacrifice the correctness of the matching. To contend with this challenge, we propose and formalize the concept of similarity-based bucketization~(SBB). With SBB, a client reveals a small amount of information to a database-holding server so that it can generate a bucket of potentially similar items. The bucket is small enough for efficient application of privacy-preserving protocols for similarity. To analyze the privacy risk of the revealed information, we introduce a framework for measuring an adversary's confidence in inferring a predicate about the client input correctly. We develop a practical SBB protocol for image content, and evaluate its client privacy guarantee with real-world social media data. We then combine SBB with various similarity protocols, showing that the combination with SBB provides a speedup of at least 29x on large-scale databases compared to that without, while retaining correctness of over 95%.

cs.CR↗

arXiv2020

Guarding Serverless Applications with SecLambda

As an emerging application paradigm, serverless computing attracts attention from more and more attackers. Unfortunately, security tools for conventional applications cannot be easily ported to serverless, and existing serverless security solutions are inadequate. In this paper, we present \emph{SecLambda}, an extensible security framework that leverages local function state and global application state to perform sophisticated security tasks to protect an application. We show how SecLambda can be used to achieve control flow integrity, credential protection, and rate limiting in serverless applications. We evaluate the performance overhead and security of SecLambda using realistic open-source applications, and our results suggest that SecLambda can mitigate several attacks while introducing relatively low performance overhead.

cs.CR↗

arXiv2020

The Tools and Tactics Used in Intimate Partner Surveillance: An Analysis of Online Infidelity Forums

Abusers increasingly use spyware apps, account compromise, and social engineering to surveil their intimate partners, causing substantial harms that can culminate in violence. This form of privacy violation, termed intimate partner surveillance (IPS), is a profoundly challenging problem to address due to the physical access and trust present in the relationship between the target and attacker. While previous research has examined IPS from the perspectives of survivors, we present the first measurement study of online forums in which (potential) attackers discuss IPS strategies and techniques. In domains such as cybercrime, child abuse, and human trafficking, studying the online behaviors of perpetrators has led to better threat intelligence and techniques to combat attacks. We aim to provide similar insights in the context of IPS. We identified five online forums containing discussion of monitoring cellphones and other means of surveilling an intimate partner, including three within the context of investigating relationship infidelity. We perform a mixed-methods analysis of these forums, surfacing the tools and tactics that attackers use to perform surveillance. Via qualitative analysis of forum content, we present a taxonomy of IPS strategies used and recommended by attackers, and synthesize lessons for technologists seeking to curb the spread of IPS.

cs.CR↗

arXiv2020

Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections

Adversarial interactions against politicians on social media such as Twitter have significant impact on society. In particular they disrupt substantive political discussions online, and may discourage people from seeking public office. In this study, we measure the adversarial interactions against candidates for the US House of Representatives during the run-up to the 2018 US general election. We gather a new dataset consisting of 1.7 million tweets involving candidates, one of the largest corpora focusing on political discourse. We then develop a new technique for detecting tweets with toxic content that are directed at any specific candidate.Such technique allows us to more accurately quantify adversarial interactions towards political candidates. Further, we introduce an algorithm to induce candidate-specific adversarial terms to capture more nuanced adversarial interactions that previous techniques may not consider toxic. Finally, we use these techniques to outline the breadth of adversarial interactions seen in the election, including offensive name-calling, threats of violence, posting discrediting information, attacks on identity, and adversarial message repetition.

cs.HC↗

arXiv2020

Characterizing Twitter Users Who Engage in Adversarial Interactions against Political Candidates

Social media provides a critical communication platform for political figures, but also makes them easy targets for harassment. In this paper, we characterize users who adversarially interact with political figures on Twitter using mixed-method techniques. The analysis is based on a dataset of 400~thousand users' 1.2~million replies to 756 candidates for the U.S. House of Representatives in the two months leading up to the 2018 midterm elections. We show that among moderately active users, adversarial activity is associated with decreased centrality in the social graph and increased attention to candidates from the opposing party. When compared to users who are similarly active, highly adversarial users tend to engage in fewer supportive interactions with their own party's candidates and express negativity in their user profiles. Our results can inform the design of platform moderation mechanisms to support political figures countering online harassment.

cs.HC↗

arXiv2019

Protocols for Checking Compromised Credentials

To prevent credential stuffing attacks, industry best practice now proactively checks if user credentials are present in known data breaches. Recently, some web services, such as HaveIBeenPwned (HIBP) and Google Password Checkup (GPC), have started providing APIs to check for breached passwords. We refer to such services as compromised credential checking (C3) services. We give the first formal description of C3 services, detailing different settings and operational requirements, and we give relevant threat models. One key security requirement is the secrecy of a user's passwords that are being checked. Current widely deployed C3 services have the user share a small prefix of a hash computed over the user's password. We provide a framework for empirically analyzing the leakage of such protocols, showing that in some contexts knowing the hash prefixes leads to a 12x increase in the efficacy of remote guessing attacks. We propose two new protocols that provide stronger protection for users' passwords, implement them, and show experimentally that they remain practical to deploy.

cs.CR↗

arXiv2018

When Textbook RSA is Used to Protect the Privacy of Hundreds of Millions of Users

We evaluate Tencent's QQ Browser, a popular mobile browser in China with hundreds of millions of users---including 16 million overseas, with respect to the threat model of a man-in-the-middle attacker with state actor capabilities. This is motivated by information in the Snowden revelations suggesting that another Chinese mobile browser, UC Browser, was being used to track users by Western nation-state adversaries. Among the many issues we found in QQ Browser that are presented in this paper, the use of "textbook RSA"---that is, RSA implemented as shown in textbooks, with no padding---is particularly interesting because it affords us the opportunity to contextualize existing research in breaking textbook RSA. We also present a novel attack on QQ Browser's use of textbook RSA that is distinguished from previous research by its simplicity. We emphasize that although QQ Browser's cryptography and our attacks on it are very simple, the impact is serious. Thus, research into how to break very poor cryptography (such as textbook RSA) has both pedagogical value and real-world impact.

cs.CR↗

arXiv2017

Machine Learning Models that Remember Too Much

Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data. We consider a malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model. In this setting, we design and implement practical algorithms, some of them very similar to standard ML techniques such as regularization and data augmentation, that "memorize" information about the training dataset in the model yet the model is as accurate and predictive as a conventionally trained model. We then explain how the adversary can extract memorized information from the model. We evaluate our techniques on standard ML tasks for image classification (CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20 Newsgroups and IMDB). In all cases, we show how our algorithms create models that have high predictive power yet allow accurate extraction of subsets of their training data.

cs.CR↗

arXiv2016

Network Traffic Obfuscation and Automated Internet Censorship

Internet censors seek ways to identify and block internet access to information they deem objectionable. Increasingly, censors deploy advanced networking tools such as deep-packet inspection (DPI) to identify such connections. In response, activists and academic researchers have developed and deployed network traffic obfuscation mechanisms. These apply specialized cryptographic tools to attempt to hide from DPI the true nature and content of connections. In this survey, we give an overview of network traffic obfuscation and its role in circumventing Internet censorship. We provide historical and technical background that motivates the need for obfuscation tools, and give an overview of approaches to obfuscation used by state of the art tools. We discuss the latest research on how censors might detect these efforts. We also describe the current challenges to censorship circumvention research and identify concrete ways for the community to address these challenges.

cs.NI↗