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Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems

Donghyun Lee, Mo Tiwari

arXiv:2410.07283v1cs.MAcs.AIcs.CR

TL;DR

Prior safety research has focused mainly on single-agent prompt injection, leaving risks in multi-agent systems less understood. The paper introduces Prompt Infection, a self-replicating attack across agents, and evaluates LLM Tagging as a defense. Experiments report susceptibility across scenarios and find that tagging combined with other safeguards mitigates infection.

  • Problem

    Prompt-injection research has focused mainly on single-agent systems, leaving the risks of extending attacks to shielded agents in multi-agent systems less clear.

    Source: §1 · pp. 1–2

  • Method

    Prompt Infection self-replicates from a compromised agent to other agents, while LLM Tagging marks agent responses to help downstream agents distinguish them from user inputs.

    Source: §1 · p. 2

  • Results

    Self-replicating infections consistently outperformed non-replicating attacks across most scenarios, and compromised GPT-4o executed malicious prompts more efficiently than GPT-3.5.

    Source: §8 · p. 10

  • Takeaways & Limitations

    LLM Tagging combined with techniques like marking and instruction defense significantly reduced infection, while multi-agent systems remain vulnerable when memory retrieval is unsecured.

    Source: §8 · p. 10

    Evaluation focused on GPT-family models and basic multi-agent architectures; tagging used handcrafted attacks, while algorithmically generated prompts can bypass such defenses.

    Source: §7 · p. 10

Abstract

from arXiv · show

As Large Language Models (LLMs) grow increasingly powerful, multi-agent systems are becoming more prevalent in modern AI applications. Most safety research, however, has focused on vulnerabilities in single-agent LLMs. These include prompt injection attacks, where malicious prompts embedded in external content trick the LLM into executing unintended or harmful actions, compromising the victim's application. In this paper, we reveal a more dangerous vector: LLM-to-LLM prompt injection within multi-agent systems. We introduce Prompt Infection, a novel attack where malicious prompts self-replicate across interconnected agents, behaving much like a computer virus. This attack poses severe threats, including data theft, scams, misinformation, and system-wide disruption, all while propagating silently through the system. Our extensive experiments demonstrate that multi-agent systems are highly susceptible, even when agents do not publicly share all communications. To address this, we propose LLM Tagging, a defense mechanism that, when combined with existing safeguards, significantly mitigates infection spread. This work underscores the urgent need for advanced security measures as multi-agent LLM systems become more widely adopted.

1 INTRODUCTION

Prompt Infection extends prompt injection from single agents to multi-agent systems, where a compromised agent can spread malicious instructions and coordinate other agents. Experiments find broad susceptibility, while LLM Tagging is most effective when combined with traditional defenses.

1 INTRODUCTION

  • Prior prompt-injection research focused mainly on single-agent systems, leaving risks in multi-agent settings and the spread of breaches to shielded agents poorly understood.Prompt injection can let attackers access sensitive data, spread propaganda, disrupt operations, or trick users into clicking malicious URLs.Source: §1 · pp. 1–2
  • Prompt Infection uses self-replication to spread from a compromised agent, coordinating agents to exchange data and issue tool-specific instructions.The paper frames this as a way for a single vulnerability to escalate into widespread system compromise.Source: §1 · p. 2
  • Multi-agent systems proved highly susceptible: compromised agents collaborated in data theft, infections spread logistically in social simulations, and more capable models executed attacks more effectively.GPT-4o was not inherently safer than GPT-3.5 Turbo when compromised.Source: §1 · p. 2
  • LLM Tagging marks agent responses so downstream agents can distinguish them from user inputs, but it is insufficient alone; combined with traditional defenses, it mitigates infection.The paper reports that neither LLM Tagging nor traditional defenses alone prevent LLM-to-LLM prompt injection.Source: §1 · p. 2
  • The findings challenge the assumption that distributed architecture makes multi-agent systems inherently safer, since agents can attack and compromise one another.Source: §1 · p. 2

2 RELATED WORKS

Prior work studies prompt injection across single-agent applications and several attack forms, while multi-agent safety research has examined robustness and behavior manipulation. The paper distinguishes its focus on self-replicating prompt injection across agents from related multi-agent attacks.

Prompt Injection

  • Prompt-injection research spans real-world applications, robotics, automated prompt generation, indirect attacks, single-agent AI worms, and image-based attacks; defenses include model fine-tuning and prompt-based methods.StruQ and Signed Prompt are described as limited to open-source models, while Spotlighting applies to black-box models.Source: §2 · p. 2

Safety in Multi-Agent Systems

  • Multi-agent safety work covers robustness testing, false information and errors, malicious behavior, and prompt injection, but cited attacks lack self-replication or are restricted to adversarial images.Zhang et al. (2024a) focuses on availability attacks in two agents; Gu et al. (2024) targets multimodal models with image-retrieving tools.Source: §2 · p. 2

3 PROMPT INFECTION

Prompt Infection is a self-replicating prompt injection that begins in contaminated external content and propagates through a multi-agent system. Its mechanisms support attacks ranging from data theft to concealed malware delivery.

3 PROMPT INFECTION

  • A malicious prompt embedded in a PDF, email, or webpage can compromise the first agent that processes it and replicate through the multi-agent system.Source: §3 · p. 3

3.1 MECHANISM

  • Prompt hijacking makes a victim agent disregard its original instructions, while self-replication transmits the infection prompt to the next agent.The infection can reduce agents' distinct task functions to a repetitive payload-driven recursive cycle.Source: §3.1 · p. 3
  • The shared Data note accumulates information as the infection passes between agents, including tool information or sensitive data intended for an agent that can communicate externally.Payloads assign tasks according to agents' roles and tools, such as extracting data or transmitting it to an external server.Source: §3.1 · p. 3

3.2 ATTACK SCENARIOS

  • Prompt Infection is presented as a means to pursue content manipulation, malware spread, scams, availability attacks, and data theft across multi-agent systems.Source: §3.2 · pp. 3–4
  • In the data-theft scenario, a Web Reader propagates the infection, a DB Manager retrieves internal documents and appends them to the prompt, and a Coder writes code to exfiltrate the data.The figure describes agents with different tools collaborating to exfiltrate data.Source: §3.2 · p. 4, Figure 2 · p. 4
  • For a concealed malware attack, agents propagate the infection until the final agent, which omits self-replication and instructs the user to click a malicious URL.The figure identifies skipping the final self-replication step as a way to hide the attack prompt.Source: §3.2 · p. 4, Figure 3 · p. 4
  • The paper points readers to Appendix A for the full functional Prompt Infection prompt.Source: §3.2 · p. 4

4 EXPERIMENT SETUP

The experiments model both tool-using multi-agent applications and a social simulation, with separate infection setups and success criteria. They compare self-replicating attacks with a non-replicating baseline and model iterative spread through dialogue and memory retrieval.

4.1 MULTI-AGENT APPLICATIONS

  • The application simulation uses GPT-4o and GPT-3.5 Turbo, tool-specific first agents, and subsequent agents with roles such as strategist, summarizer, editor, and writer; communication is global or local.Local messaging gives agents only partial histories from predecessors and is described as harder for infection to propagate through.Source: §4.1 · p. 5
  • The application dataset pairs 120 instructions across email, PDF, and web scenarios with synthetic documents, yielding 360 instruction–attack phrase pairs for scams, content manipulation, and malware; data theft uses synthetic CSV data.Web attack prompts are injected into retrieved simulated documents, not actual websites.Source: §4.1 · p. 5
  • Data-theft success requires compromising the first agent, retrieving sensitive data through a CSV reader, and writing a POST request through a coder; other attacks require concealed malicious output from the final agent.Source: §4.1 · p. 5
  • The non-replicating baseline passes an instruction from the first agent to the second without self-replication, enabling comparison of infection spread.Source: §4.1 · p. 5

4.2 SOCIETY OF AGENTS

  • The social simulation uses 10–50 agents in random pairwise dialogues, starts with one compromised citizen, and models infection spreading through dialogue.Memory retrieval selects the top three memories by importance, relevance, and recency; memory is not explicitly shared between agents, so prompts must spread iteratively.Source: §4.2 · p. 5

5 RESULTS

Self-replicating infection generally improves attacks in global messaging, while results depend on the threat and messaging mode. In open agent societies, infections spread more efficiently as populations grow, and manipulating memory importance can sustain their spread.

5.1 PROMPT INFECTION AGAINST MULTI-AGENT APPLICATIONS

  • Self-replication raises success by 13.92% for GPT-4o and 209% for GPT-3.5 across most global scam, malware, and manipulation cases, but data theft reverses by 8.48% as agents increase.The data-theft reversal is an average advantage for non-replicating infection as the number of agents increases.Source: Figure 4 · p. 6, §5.1 · p. 6
  • In local messaging, self-replicating success is about 20% lower than global, yet it alone scales beyond two agents; non-replicating infection struggles past two.Local infection fails if even one agent is uncompromised, whereas global messaging can spread through shared message history.Source: §5.1 · p. 6
  • GPT-4o ignores 66% of self-replicating and 54% of non-replicating attacks, versus 9% and 20% for GPT-3.5, yet executes malicious tasks more precisely once compromised.The failure analysis also reports fewer GPT-4o mixed-action and deformed-infection failures, while GPT-3.5 has more no-action and agent-error failures.Source: §5.1 · pp. 6–7, Figure 5 · p. 7

5.2 PROMPT INFECTION AGAINST SOCIETY OF AGENTS

  • In evolving decentralized societies, infection initially grows exponentially-like, then logistically; full infection takes a smaller share of turns in larger populations.Full infection occurs at about 47% of turns for 10 agents and 21.4% for 50 agents.Source: §5.2 · p. 7, Figure 6 · p. 8
  • Embedding an importance-rating manipulation prompt yielded scores of 10 for GPT-4o and 9.84 for GPT-3.5; without it, scores fell to 1.94 and 1.00, and infection died out after K = 3 turns.Source: §5.2 · p. 8

6 DEFENSES

The section proposes LLM Tagging, which marks agent-originated responses, and evaluates it alongside repurposed prompt-injection defenses. Tagging alone and existing defenses alone provide limited protection, while selected combinations sharply reduce attack success.

6 DEFENSES

  • LLM Tagging prepends an “[AGENT NAME]:” marker to agent responses, and the section repurposes existing tool-to-LLM defenses for infection scenarios using approaches that do not require model-parameter access.Table 2 presents defense strategies against traditional prompt injection repurposed for LLM-to-LLM prompt injection.Source: §6 · pp. 8–9, Table 2 · p. 9
  • Marking + LLM Tagging prevents all attacks, Instruction Defense + LLM Tagging reduces attack success to 3%, and Sandwich + LLM Tagging lowers it to 16%.The experiments find that combining LLM Tagging with other defenses enhances protection against LLM-to-LLM prompt injections.Source: §6 · p. 9
  • LLM Tagging alone reduces attack success by only 5%, and none of the tested defenses is particularly effective in isolation.Traditional prompt injections can still occur even when the model is told the source of external inputs.Source: §6 · p. 9
  • Marking still permits 76% of attacks, while delimiting data and sandwiching allow nearly all attacks; Figure 7 compares average attack success rates by defense type with and without Tagging.The authors conclude that pairing Tagging with defenses such as marking or instruction defense is crucial for mitigating prompt infections.Source: §6 · p. 9, Figure 7 · p. 9

7 LIMITATIONS AND FUTURE WORK

The evaluation centers on GPT-family models and basic multi-agent architectures, while other models and more complex systems remain less explored. The authors also identify handcrafted attacks and exposed prompts as boundaries for their defense and threat analysis.

7 LIMITATIONS AND FUTURE WORK

  • Experiments focused on the GPT family, leaving Claude, Llama, and Gemini underexplored; preliminary Claude tests showed similar vulnerabilities, but full results were unavailable due to computational costs.Prior research suggests the findings may generalize.Source: §7 · p. 10
  • The study primarily examined basic multi-agent architectures, while the authors expect infection may also spread through communication in more complex systems.They attribute this potential reach to self-replication wherever agents communicate.Source: §7 · p. 10
  • LLM Tagging was tested against handcrafted attacks, although studies report algorithmically generated prompts can bypass such defenses (Liu et al., 2024a; Mehrotra et al., 2024).The authors identify a need for stronger countermeasures; exposed attack prompts also offer detection opportunities but motivate stealthier methods.Source: §7 · p. 10

8 CONCLUSION

The conclusion presents Prompt Infection as a self-replicating attack on LLM-based multi-agent systems and reports vulnerabilities across several settings. It says combining LLM Tagging with defenses such as marking and instruction defense reduced infection success rates.

8 CONCLUSION

  • Prompt Infection uses self-replication to propagate across LLM-based multi-agent systems, with risks including data theft, malicious actions, and system disruption.The conclusion says self-replicating infections consistently outperformed non-replicating attacks across most scenarios.Source: §8 · p. 10
  • More advanced models such as GPT-4o executed malicious prompts more efficiently than GPT-3.5, and social simulations and games were vulnerable, especially with unsecured memory retrieval.Source: §8 · p. 10
  • Combining LLM Tagging with techniques such as marking and instruction defense significantly reduced infection success rates.The conclusion emphasizes that threats can arise internally, as agents exploit one another.Source: §8 · p. 10

ETHICAL STATEMENT

The ethical statement describes disclosure as a way to encourage defense research and transparency while setting safeguards against misuse.

ETHICAL STATEMENT

  • The authors disclose the explored vulnerabilities and attacks to encourage rigorous defense research and transparency about LLM security risks.Source: ETHICAL STATEMENT · p. 10
  • They avoided injecting prompts into publicly accessible systems to prevent unintended use by others.Source: ETHICAL STATEMENT · p. 10
  • The authors say the disclosed techniques and prompts should not be used maliciously or against real-world applications without proper authorization.Source: ETHICAL STATEMENT · p. 10

A INFECTION PROMPTS

Figure 8 presents a functional self-replication infection prompt that can compromise a PDF reader agent and infect downstream agents. The prompt is flagged against malicious or unauthorized real-world use.

A INFECTION PROMPTS

  • The prompt is identified as functional and must not be used maliciously or in real-world applications without proper authorization.Source: A INFECTION PROMPTS · p. 14
  • Figure 8 shows an infection prompt embedded in PDF contents compromising the PDF reader agent and infecting downstream agents.The infectious prompts are wrapped between ‘[[[‘ and ‘]]]‘.Source: Figure 8 · p. 14
  • The downstream agents are infected with the prompts wrapped between ‘[[[‘ and ‘]]]‘.Source: Figure 8 · p. 14
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