Defeating Prompt Injections by Design
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.
67e24a997210beea5ecae330
Edoardo Debenedetti
admin_assigned
2025-03-25T09:26:38.111Z
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Edoardo Debenedetti
user
dedeswim
67e24a997210beea5ecae331
Ilia Shumailov
admin_assigned
2025-03-25T09:27:10.362Z
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i
user
iliashum
67e24a997210beea5ecae332
Tianqi Fan
67e24a997210beea5ecae333
Jamie Hayes
67e24a997210beea5ecae334
Nicholas Carlini
admin_assigned
2025-03-25T09:27:26.752Z
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Nicholas Carlini
user
carlini
67e24a997210beea5ecae335
Daniel Fabian
67e24a997210beea5ecae336
Christoph Kern
67e24a997210beea5ecae337
Chongyang Shi
67e24a997210beea5ecae338
Andreas Terzis
67e24a997210beea5ecae339
Florian Tramèr
admin_assigned
2025-03-25T09:27:51.364Z
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Florian Tramer
user
ftramer
Defeating Prompt Injections by Design
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.
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.
LLMs prompt injection attacks CaMeL protective system layer control flows data flows exfiltration of private data capability AgentDojo