title: Defeating Prompt Injections by Design
summary: Large Language Models (LLMs) are increasingly deployed in agentic systems
  that interact with an external environment. However, LLM agents are vulnerable
  to prompt injection attacks when handling untrusted data. In this paper we
  propose CaMeL, a robust defense that creates a protective system layer around
  the LLM, securing it even when underlying models may be susceptible to attacks.
  To operate, CaMeL explicitly extracts the control and data flows from the
  (trusted) query; therefore, the untrusted data retrieved by the LLM can never
  impact the program flow. To further improve security, CaMeL relies on a notion
  of a capability to prevent the exfiltration of private data over unauthorized
  data flows. We demonstrate effectiveness of CaMeL by solving 67% of tasks
  with provable security in AgentDojo [NeurIPS 2024], a recent agentic security
  benchmark.
paper_authors._id: 67e24a997210beea5ecae330
paper_authors.name: Edoardo Debenedetti
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paper_authors.fullname: Edoardo Debenedetti
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paper_authors._id: 67e24a997210beea5ecae331
paper_authors.name: Ilia Shumailov
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paper_authors._id: 67e24a997210beea5ecae332
paper_authors.name: Tianqi Fan
paper_authors._id: 67e24a997210beea5ecae333
paper_authors.name: Jamie Hayes
paper_authors._id: 67e24a997210beea5ecae334
paper_authors.name: Nicholas Carlini
paper_authors.status: admin_assigned
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paper_authors.fullname: Nicholas Carlini
paper_authors.type: user
paper_authors.user: carlini
paper_authors._id: 67e24a997210beea5ecae335
paper_authors.name: Daniel Fabian
paper_authors._id: 67e24a997210beea5ecae336
paper_authors.name: Christoph Kern
paper_authors._id: 67e24a997210beea5ecae337
paper_authors.name: Chongyang Shi
paper_authors._id: 67e24a997210beea5ecae338
paper_authors.name: Andreas Terzis
paper_authors._id: 67e24a997210beea5ecae339
paper_authors.name: Florian Tramèr
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:27:51.364Z
paper_authors._id: 63568f18ba90b4ea9fe91cb5
paper_authors.avatarUrl: /avatars/3e8b3c573e20cf80d329a312bfc34728.svg
paper_authors.fullname: Florian Tramer
paper_authors.type: user
paper_authors.user: ftramer
paper_title: Defeating Prompt Injections by Design
paper_summary: Large Language Models (LLMs) are increasingly deployed in agentic systems
  that interact with an external environment. However, LLM agents are vulnerable
  to prompt injection attacks when handling untrusted data. In this paper we
  propose CaMeL, a robust defense that creates a protective system layer around
  the LLM, securing it even when underlying models may be susceptible to attacks.
  To operate, CaMeL explicitly extracts the control and data flows from the
  (trusted) query; therefore, the untrusted data retrieved by the LLM can never
  impact the program flow. To further improve security, CaMeL relies on a notion
  of a capability to prevent the exfiltration of private data over unauthorized
  data flows. We demonstrate effectiveness of CaMeL by solving 67% of tasks
  with provable security in AgentDojo [NeurIPS 2024], a recent agentic security
  benchmark.
paper_ai_summary: CaMeL defends LLM agents from prompt injection attacks by segregating control and data flows and preventing unauthorized data exfiltration, achieving 67% secure task completion in AgentDojo.
paper_ai_keywords: LLMs prompt injection attacks CaMeL protective system layer control flows data flows exfiltration of private data capability AgentDojo