Send and Pretend: Exploiting Transcript Consistency Issues in End-to-End Encrypted Group Chats

• URL: http://arxiv.org/abs/2607.27510v1
• Date: Wed, 29 Jul 2026 22:54:21 GMT
• Title: Send and Pretend: Exploiting Transcript Consistency Issues in End-to-End Encrypted Group Chats
• Abstract summary: Threema, WhatsApp, Signal and iMessage have recently introduced polls to aid agreement processes in groups.<n>This implicitly sets the expectation that all participants see the same outcome and thus have the same view of the conversation.<n>In this work, we demonstrate that today's major E2EE messengers do not guarantee any form of transcript consistency for group chats.
• Score: 1.8831329126144525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: End-to-end encrypted (E2EE) messaging apps are widely praised for their security and thus also used for sensitive coordination in group chats (e.g., by political decision makers). After Threema and WhatsApp, also Signal and iMessage have recently introduced polls to aid agreement processes in groups. This implicitly sets the expectation that all participants see the same outcome and thus have the same view of the conversation. This property is commonly referred to as transcript consistency (TC). In this work, we demonstrate that today's major E2EE messengers do not guarantee any form of TC for group chats, allowing a malicious group member to selectively omit, reorder, or present altered content to different recipients without triggering warnings in their user interface. We systematically investigate the extent of the problem under a malicious-participant threat model that targets the integrity of the shared transcript, or inconsistent delivery across a user's linked devices. We identify multiple equivocation vectors that range from protocol fallback paths to deliberate use of pairwise delivery channels within groups. We demonstrate concrete exploitation scenarios such as social engineering, evading moderation, and, in particular, rigging polls. Beyond these cross-service design issues, we also uncover implementation-specific behaviors with privacy implications (e.g., device OS fingerprinting). Finally, we contextualize our findings within prior transcript-consistency research and outline practical low-overhead mitigations and UI signaling strategies that can be integrated into state-of-the-art E2EE group protocols.

Related papers

• Automatic Red Teaming for Implicit Vulnerabilities of Text-to-Image Models [58.59008920408855]
  We propose Adversarial Probing for Implicit VulnErabilities (AdvPIE) to expose implicit vulnerabilities without requiring access to the parameters of target models.<n> AdvPIE adopts a policy agent to generate and refine implicit adversarial prompts based on the feedback from a judge agent.<n>Experiments on standard and safety-aligned T2I models show that AdvPIE1 effectively uncovers implicit vulnerabilities.
  arXiv  Detail & Related papers  (2026-09-05T13:45:09Z)
• SoK: Intent-Oriented Systematization of Multi-Turn LLM Jailbreaks [69.08780911753102]
  Multi-turn jailbreaks exploit a pattern by advancing a harmful intent across turns, so that no single message exposes the full objective.<n>We develop a four-part, intent-oriented taxonomy that organizes multi-turn jailbreaks by adversarial intent structure.
  arXiv  Detail & Related papers  (2026-08-02T09:24:08Z)
• One Turn Too Late: Response-Aware Defense Against Hidden Malicious Intent in Multi-Turn Dialogue [55.98008208209856]
  Hidden malicious intent in multi-turn dialogue poses a growing threat to large language models (LLMs)<n>Recent studies show that even modern commercial models with advanced guardrails remain vulnerable to such attacks despite advances in safety alignment and external guardrails.<n>In this work, we address this challenge by detecting the earliest turn at which delivering the candidate response would make the accumulated interaction sufficient to enable harmful action.
  arXiv  Detail & Related papers  (2026-05-07T03:35:31Z)
• More Than Sum of Its Parts: Deciphering Intent Shifts in Multimodal Hate Speech Detection [49.07221734365168]
  Hate speech on social media is critical for securing cyberspace, yet relies heavily on automated detection systems.<n>We characterize semantic intent shifts where modalities interact to construct implicit hate from benign cues or neutralize toxicity through semantic inversion.<n>We propose the Asymmetric Reasoning via Courtroom Agent DEbate framework to effectively decipher these complex cues.
  arXiv  Detail & Related papers  (2026-03-22T15:46:23Z)
• Synthetic Voices, Real Threats: Evaluating Large Text-to-Speech Models in Generating Harmful Audio [63.18443674004945]
  This work explores a content-centric threat: exploiting TTS systems to produce speech containing harmful content.<n>We present HARMGEN, a suite of five attacks organized into two families that address these challenges.
  arXiv  Detail & Related papers  (2025-11-14T03:00:04Z)
• Transcript Franking for Encrypted Messaging [14.460663943390266]
  We introduce transcript franking, a new type of protocol that allows reporting subsets of conversations.<n>We define syntax, semantics, and security for transcript franking in two-party and group messaging.
  arXiv  Detail & Related papers  (2025-07-25T15:50:42Z)
• User Perceptions and Attitudes Toward Untraceability in Messaging Platforms [3.87707864695882]
  "Untraceability" is preventing third parties from tracing who communicates with whom.<n>This paper explores user perceptions of and attitudes toward "untraceability"<n>We identify a diverse set of features that users perceive to be useful for untraceable messaging.
  arXiv  Detail & Related papers  (2025-06-12T18:19:50Z)
• Information Bargaining: Bilateral Commitment in Bayesian Persuasion [60.3761154043329]
  We introduce a unified framework and a well-structured solution concept for long-term persuasion.<n>This perspective makes explicit the common knowledge of the game structure and grants the receiver comparable commitment capabilities.<n>The framework is validated through a two-stage validation-and-inference paradigm.
  arXiv  Detail & Related papers  (2025-06-06T08:42:34Z)
• Synopsis: Secure and private trend inference from encrypted semantic embeddings [2.7998963147546148]
  We introduce Synopsis, a secure architecture for analyzing messaging trends in consensually-donated E2EE messages using message embeddings.<n>Since the goal of this system is investigative journalism, Synopsis must facilitate both exploratory and targeted analyses.<n> Evaluations on a dataset of Hindi-language WhatsApp messages demonstrate the efficiency and accuracy of our approach.
  arXiv  Detail & Related papers  (2025-05-29T17:34:10Z)
• Accountable, Scalable and DoS-resilient Secure Vehicular Communication [0.27624021966289597]
  broadcasted Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs) are pseudonymous authenticated for security and privacy protection.<n>This creates an asymmetry that can be easily exploited by external adversaries to launch a clogging Denial of Service (DoS) attack.<n>We propose efficient cryptographic constructs, which we term message verification facilitators, to prioritize processing resources for verification of potentially valid messages.
  arXiv  Detail & Related papers  (2025-05-28T09:25:34Z)
• Metadata-private Messaging without Coordination [20.481776420813915]
  PingPong is an end-to-end system for metadata-private messaging.<n>It replaces the rigid "dial-before-converse" paradigm with a more flexible "notify-before-retrieval" workflow.<n>Pong achieves a level of usability akin to modern instant messaging systems.
  arXiv  Detail & Related papers  (2025-04-28T08:21:16Z)
• Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning [58.57194301645823]
  Large language models (LLMs) are increasingly integrated into real-world personalized applications.<n>The valuable and often proprietary nature of the knowledge bases used in RAG introduces the risk of unauthorized usage by adversaries.<n>Existing methods that can be generalized as watermarking techniques to protect these knowledge bases typically involve poisoning or backdoor attacks.<n>We propose name for harmless' copyright protection of knowledge bases.
  arXiv  Detail & Related papers  (2025-02-10T09:15:56Z)
• Certifying LLM Safety against Adversarial Prompting [70.96868018621167]
  Large language models (LLMs) are vulnerable to adversarial attacks that add malicious tokens to an input prompt.<n>We introduce erase-and-check, the first framework for defending against adversarial prompts with certifiable safety guarantees.
  arXiv  Detail & Related papers  (2023-09-06T04:37:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.