AI Snitches Get Glitches: Towards Evading Agentic Surveillance

• URL: http://arxiv.org/abs/2606.25836v2
• Date: Fri, 26 Jun 2026 07:45:10 GMT
• Title: AI Snitches Get Glitches: Towards Evading Agentic Surveillance
• Abstract summary: We introduce and formalize the problem of agentic surveillance: the ability of an AI agent to analyze available information, craft a report, and send it out using available tools.<n>We find that some models exhibit emergent (i.e., unprompted) tendencies to help surveillance, but they also report the attempts to surveil users to repurpose the government.<n>We conclude that agentic surveillance can already be easily implemented and, therefore, call for a comprehensive technical, ethical, and legislative framework to protect users.
• Score: 17.665262489915044
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To better assist users with completing challenging tasks, AI agents mediate communications, access data, and interact with different APIs. Many employers (and even nation-states) already provide their users with this technology. However, widespread adoption of AI agents creates a new risk to abuse access to user data for another goal: surveilling users. These users might not even have the ability or permission to control the actions and data accesses of the surveilling agents. We introduce and formalize the problem of agentic surveillance: the ability of an AI agent to analyze available information, craft a report, and send it out using available tools. To evaluate surveillance capabilities across different models, we create SurveilBench, a dataset of various reporting scenarios focusing on three domains: corporate, education, and police. We find that some models exhibit emergent (i.e., unprompted) tendencies to help surveillance, but they also report the attempts to surveil users to the government. Finally, we repurpose prompt injections for evading surveillance and develop three evasion techniques that hide from, deceive, or induce over-escalation in surveillance agents. We conclude that agentic surveillance can already be easily implemented and, therefore, call for a comprehensive technical, ethical, and legislative framework to protect users.

Related papers

• When Agents See Differently: Exposing UI Desynchronization Threats in Mobile Agents [31.736012043129495]
  We show that users and agents observe consistent information from the same interface.<n>The same UI state can present materially different information to users and agents, a mismatch we term human-agent UI desynchronization.<n>We develop an automated framework that constructs user runtime instruction-agnostic UI desynchronization attacks and realizes them in deployable APKs.
  arXiv  Detail & Related papers  (2026-09-15T07:02:10Z)
• SoK: The Attack Surface of Agentic AI -- Tools, and Autonomy [3.340255811686752]
  Recent AI systems combine large language models with tools, external knowledge via retrieval-augmented generation (RAG)<n>This agentic AI paradigm greatly expands capabilities - but also vastly enlarges the attack surface.<n>We develop a comprehensive taxonomy of attacks spanning prompt-level injections, knowledge-base poisoning, tool/plug-in exploits, and multi-agent emergent threats.
  arXiv  Detail & Related papers  (2026-03-24T08:21:51Z)
• Agents of Chaos [50.53354213047402]
  We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment.<n>Twenty AI researchers interacted with the agents under benign and adversarial conditions.<n>Our findings establish the existence of security-, privacy-, and governance-relevant vulnerabilities in realistic deployment settings.
  arXiv  Detail & Related papers  (2026-02-23T16:28:48Z)
• OMNI-LEAK: Orchestrator Multi-Agent Network Induced Data Leakage [59.3826294523924]
  We investigate the security vulnerabilities of a popular multi-agent pattern known as the orchestrator setup.<n>We report the susceptibility of frontier models to different categories of attacks, finding that both reasoning and non-reasoning models are vulnerable.
  arXiv  Detail & Related papers  (2026-02-13T21:32:32Z)
• What's the next frontier for Data-centric AI? Data Savvy Agents [71.76058707995398]
  We argue that data-savvy capabilities should be a top priority in the design of agentic systems.<n>We propose four key capabilities to realize this vision: Proactive data acquisition, Sophisticated data processing, Interactive test data synthesis, and Continual adaptation.
  arXiv  Detail & Related papers  (2025-11-02T17:09:29Z)
• Malice in Agentland: Down the Rabbit Hole of Backdoors in the AI Supply Chain [82.98626829232899]
  Fine-tuning AI agents on data from their own interactions introduces a critical security vulnerability within the AI supply chain.<n>We show that adversaries can easily poison the data collection pipeline to embed hard-to-detect backdoors.
  arXiv  Detail & Related papers  (2025-10-03T12:47:21Z)
• AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents [66.29263282311258]
  We introduce a new benchmark AgentDAM that measures if AI web-navigation agents follow the privacy principle of data minimization''<n>Our benchmark simulates realistic web interaction scenarios end-to-end and is adaptable to all existing web navigation agents.
  arXiv  Detail & Related papers  (2025-03-12T19:30:31Z)
• YETI (YET to Intervene) Proactive Interventions by Multimodal AI Agents in Augmented Reality Tasks [16.443149180969776]
  Augmented Reality (AR) head worn devices can uniquely improve the user experience of solving procedural day-to-day tasks.<n>Such AR capabilities can help AI Agents see and listen to actions that users take which can relate to multimodal capabilities of human users.<n>Proactivity of AI Agents on the other hand can help the human user detect and correct any mistakes in agent observed tasks.
  arXiv  Detail & Related papers  (2025-01-16T08:06:02Z)
• TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection [59.04634695294402]
  Video anomaly detection (VAD) without human monitoring is a complex computer vision task. Privacy leakage in VAD allows models to pick up and amplify unnecessary biases related to people's personal information. We propose TeD-SPAD, a privacy-aware video anomaly detection framework that destroys visual private information in a self-supervised manner.
  arXiv  Detail & Related papers  (2023-08-21T22:42:55Z)

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.