mlsanityData to 14 Oct 2026

Invitation Is All You Need! Promptware Attacks Against LLM-Powered Assistants in Production Are Practical and Dangerous

arXiv 2508.12175 ↗First posted 16 Aug 2025cs.CRcited by 12

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Abstract (arXiv)

The growing integration of LLMs into applications has introduced new security risks, notably known as Promptware - maliciously engineered prompts designed to manipulate LLMs to compromise the CIA triad of these applications. While prior research warned about a potential shift in the threat landscape for LLM-powered applications, the risk posed by Promptware is frequently perceived as low. In this paper, we investigate the risk Promptware poses to users of Gemini-powered assistants (web application, mobile application, and Google Assistant). We propose a novel Threat Analysis and Risk Assessment (TARA) framework to assess Promptware risks for end users. Our analysis focuses on a new variant of Promptware called Targeted Promptware Attacks, which leverage indirect prompt injection via common user interactions such as emails, calendar invitations, and shared documents. We demonstrate 14 attack scenarios applied against Gemini-powered assistants across five identified threat classes: Short-term Context Poisoning, Permanent Memory Poisoning, Tool Misuse, Automatic Agent Invocation, and Automatic App Invocation. These attacks highlight both digital and physical consequences, including spamming, phishing, disinformation campaigns, data exfiltration, unapproved user video streaming, and control of home automation devices. We reveal Promptware's potential for on-device lateral movement, escaping the boundaries of the LLM-powered application, to trigger malicious actions using a device's applications. Our TARA reveals that 73% of the analyzed threats pose High-Critical risk to end users. We discuss mitigations and reassess the risk (in response to deployed mitigations) and show that the risk could be reduced significantly to Very Low-Medium. We disclosed our findings to Google, which deployed dedicated mitigations.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Aug 2025.

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Citing papers per year: 2026: 12.

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  1. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
  2. 2025LLMail-Inject: A Dataset from a Realistic Adaptive Prompt Injection Challenge
  3. 2023Prompt Injection attack against LLM-integrated Applications
  4. 2024MaPPing Your Model: Assessing the Impact of Adversarial Attacks on LLM-based Programming Assistants
  5. 2025Enhancing Security in LLM Applications: A Performance Evaluation of Early Detection Systems
  6. 2024Systematically Analyzing Prompt Injection Vulnerabilities in Diverse LLM Architectures
  7. 2025When AI Meets the Web: Prompt Injection Risks in Third-Party AI Chatbot Plugins
  8. 2026The System Prompt Is the Attack Surface: How LLM Agent Configuration Shapes Security and Creates Exploitable Vulnerabilities