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Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast
Xiangming Gu, Xiaosen Zheng, Tianyu Pang, Chao Du, Qian Liu, Ye Wang, Jing Jiang, Min Lin
TL;DR
The paper identifies infectious jailbreak, where compromising one multimodal agent can spread harmful behavior through multi-agent interaction without further adversary intervention. It formalizes and simulates this phenomenon, finding exponential infection in systems of up to one million LLaVA-1.5 agents while noting practical limitations on adversarial-image universality and recovery dynamics.
Problem
Adversarial images and prompts can jailbreak individual MLLM agents, but the safety risks of deploying interacting agents with memory and tool use remain insufficiently understood.
Method
The paper designs adversarial images and evaluates infectious jailbreak using randomized pair-wise chat, formal infectious dynamics, and simulations of up to one million LLaVA-1.5 agents with memory banks.
Results
A single adversarial image inserted into one agent’s memory is sufficient to produce exponentially fast infection resembling ideal infectious dynamics across the remaining agents.
Takeaways & Limitations
Infectious jailbreak lets an adversary pay a fixed initial cost and then spread to almost all agents in O(log N) chat rounds without further intervention.
Takeaways & Limitations
The proof-of-concept assumes universal adversarial-image conditions that practical images may not perfectly satisfy, while experiments use a specific multi-agent instantiation.
Abstract
from arXiv · showhide
A multimodal large language model (MLLM) agent can receive instructions, capture images, retrieve histories from memory, and decide which tools to use. Nonetheless, red-teaming efforts have revealed that adversarial images/prompts can jailbreak an MLLM and cause unaligned behaviors. In this work, we report an even more severe safety issue in multi-agent environments, referred to as infectious jailbreak. It entails the adversary simply jailbreaking a single agent, and without any further intervention from the adversary, (almost) all agents will become infected exponentially fast and exhibit harmful behaviors. To validate the feasibility of infectious jailbreak, we simulate multi-agent environments containing up to one million LLaVA-1.5 agents, and employ randomized pair-wise chat as a proof-of-concept instantiation for multi-agent interaction. Our results show that feeding an (infectious) adversarial image into the memory of any randomly chosen agent is sufficient to achieve infectious jailbreak. Finally, we derive a simple principle for determining whether a defense mechanism can provably restrain the spread of infectious jailbreak, but how to design a practical defense that meets this principle remains an open question to investigate. Our project page is available at https://sail-sg.github.io/Agent-Smith/.
1. Introduction
The paper identifies infectious jailbreak as a multi-agent safety issue in which one compromised agent can spread harmful behavior to almost all agents without further adversarial intervention. Simulations show this spread can occur exponentially fast, while practical defenses remain unresolved.
- ∼100% infection is reached after 27 ∼31 chat rounds in the one-million-agent simulation, with infected agents exhibiting harmful behaviors.
- Adversarial images or prompts can jailbreak MLLMs and produce harmful behaviors.
- Multi-agent MLLM deployments add memory, tool use, and inter-agent collaboration to increasingly broad applications.
- Infectious jailbreak uses agent interaction to make infected agents inject an adversarial image into benign agents’ memory banks without external intervention.
- One adversarial image inserted into a single agent can closely reproduce infectious dynamics in simulations of up to one million LLaVA-1.5 agents.
- A defense can be assessed by a simple principle, but designing a practical defense that satisfies it remains open.
2. Related Work
Related work covers multi-agent systems built around LLMs and prior efforts to jailbreak aligned language and multimodal models. These lines of work motivate studying how agent communication may interact with jailbreak mechanisms.
- Multi-agent systems use multiple LLM-based agents for simulated behavior, information diffusion, collaboration, and downstream applications.
- The paper positions infectious jailbreak at the intersection of multi-agent interaction and jailbreaking of language or multimodal models.
- Prior jailbreaking research shows that aligned LLMs can generate objectionable content after manually designed or automatically crafted prompts.
3. Simulating Multi-Agent Environments
The paper models randomized pairwise chats among MLLM agents with text histories and image albums, then formalizes how infection and recovery evolve across rounds. Its theory predicts logarithmic spread when transmission exceeds recovery, and identifies a threshold for provable containment.
- Agent and chat pipeline: Each round randomly partitions agents into questioning and answering groups, forming N/2 pairwise chats.
- Agent and chat pipeline: The questioning agent plans, retrieves an image, generates a question, and sends the image-question pair to the answering agent.
- Agent and chat pipeline: Question-answer pairs enter both agents’ text histories, while the retrieved image enters only the answering agent’s image album.
- Infectious dynamics: An agent is infected when it carries the virus and exhibits harmful questioning or answering symptoms.
- Infectious dynamics: Transmission is modeled as unidirectional with probability β, while each infected agent recovers with probability γ per round.
- Theoretical dynamics: When β > 2γ, the virus-carrying ratio approaches a positive equilibrium exponentially; when β ≤ 2γ, it converges to zero.
- Theoretical dynamics: The rounds required to reach a target virus-carrying ratio scale as O(log N), and a defense enforcing β ≤ 2γ provably drives infection to zero asymptotically.
- Agent and chat pipeline: The simulated agent combines an MLLM, a RAG module, text histories, and an image album.
4. Experiments
Experiments show that infectious jailbreak spreads exponentially across multi-agent systems, scales to nearly one million agents, and remains effective across attack settings and model backbones. Its effectiveness varies with diversity, perturbation budgets, memory-bank sizes, and image corruptions.
- Comparing jailbreaking methods: The infectious jailbreak produces exponentially higher infection ratios than noninfectious baselines in multi-agent environments.Visual and textual prompt injections fail to infect agents, while sequential jailbreak spreads linearly.
- Scaling up N: Almost all agents are jailbroken before the 32-th chat round when scaling to one million agents from an initial virus-carrying ratio of c0 = 1/1024.Increasing the system size may slow the spread but does not make the attack fail.
- Infectious dynamics under different diversities: In high-diversity settings, infected-agent ratios approach 100% by the 24-th chat round, while larger perturbation budgets improve jailbreaking efficiency.The metrics p16 and argmin_t p_t ≥90 distinguish infection effectiveness across diversity conditions.
- Failure cases: Small perturbation budgets can produce failure cases because infected agents may not exhibit recognizable harmful symptoms despite carrying the adversarial image.The study identifies failures for border attack h < 6 and pixel attack ℓ∞, ϵ < 8/255 in high-diversity scenarios.
- Memory-bank ablations: Reducing the image album size to |B| = 2 noticeably restrains spread, whereas increasing text-history size does not significantly alter infectious dynamics.With |B| = 10, retrieval success can decrease slightly because benign images are more prevalent in the album.
- Robustness and harmful function calling: Image corruptions challenge but do not stop infectious jailbreak, and the attack remains successful on InstructBLIP as well as LLaVA-1.5.The method also makes almost all agents generate harmful JSON strings that can be parsed into function calls.
5. Conclusion
Infectious jailbreak reduces large-scale compromise to an initial jailbreak affecting a fraction p0 = O(1/N), followed by autonomous spread over approximately T = O(log N) chat rounds.
- p0 = O(1/N) initial jailbreak exposure can be followed by compromise of almost all N agents.
- T = O(log N) chat rounds are sufficient for infectious jailbreak to spread without further adversarial intervention.
Impact Statement
The study and released code could enable infectious jailbreaks that spread harmful content and trigger harmful function-calling behaviors across multimodal-agent systems.
- The methodology and code could enable users to infectiously jailbreak almost all multimodal agents in a multi-agent system.The stated risks include harmful content generation and harmful behaviors through function calling.
- A practical defense remains an open and urgent question, despite the study's proof-of-concept scope.
A. Related Work (Full Version)
Related work covers multimodal agents, multi-agent information diffusion, LLM and MLLM jailbreaking, and the infectious-dynamics setting used by this paper.
- (Multimodal) LLM agents: MLLM agents extend language-model agents with multimodal inputs and capabilities for complex task execution.
- Multi-agent systems: Multi-agent systems use communicating LLM agents for human-behavior simulation, software development, and downstream cooperation.
- Jailbreaking (M)LLMs: Prior LLM jailbreaking work shows that aligned models can be induced to generate objectionable content using manually designed or automatically crafted prompts.
- Jailbreaking (M)LLMs: Infectious jailbreak is related to in-context attacks because the virus can be saved into text histories.
- Jailbreaking MLLMs: MLLM red-teaming also attacks model alignment through adversarial images, including perturbations crafted for vision-based systems.
- Infectious dynamics: The infectious-dynamics model uses default parameters c0 = 0.5, α = 0.95, β = 0.8, and γ = 0.1.
B. Complementary Derivations of Infectious Dynamics
The paper derives infectious dynamics for randomized pairwise chat and characterizes how infection, transmission, and recovery parameters determine long-run behavior.
- Dynamics derivation: The derivation defines ct as the ratio of virus-carrying agents at chat round t and provides complete solutions for ct.
- Case β > 2γ: When β > 2γ, ct approaches 1 − 2γ/β, and the reformulated solution computes the chat rounds needed to reach a target carrying ratio.
- Case β = 2γ: When β = 2γ, ct converges to zero for any initial ratio c0.
- Case β < 2γ: When β < 2γ, ct also decreases exponentially fast to zero for any c0.
- Infection ratio: The infected-agent ratio satisfies pt = α^t ct, and under default β > 2γ it converges to α(1 − 2γ/β).
- Infection ratio: The effect of α on pt is monotonic, while β and γ determine whether infection persists or vanishes; simulations with N = 2^14 fit the theoretical results for large N.
C. Instantiation of Our Multi-agent System
The multi-agent system gives each agent a distinct identity, memory album, and role-specific prompts, then uses chat prompts to create low- and high-diversity interactions.
- Multi-agent environments: Each agent combines a shared MLLM and retrieval module with a distinct role-playing identity and personalized image album.Role descriptions include properties such as name, gender, hobby, and personality, while albums store images for future retrieval.
- Low diversity scenario: Low-diversity chats use prompts that produce short responses and limited interaction diversity.The low-diversity setup and its chat examples are shown in Figures 12 and 13.
- High diversity scenario: High-diversity chats use a different prompt configuration and generally generate longer responses.The high-diversity setup and examples are shown in Figures 14 and 15.
D. Implementation of Jailbreak Methods
The paper evaluates visual and textual prompt injection as noninfectious jailbreak baselines, using adversarial images or injected chat records to induce harmful responses and spread.
- Visual prompt injection: Visual prompt injection embeds harmful commands in an adversarial image and instructs agents to retrieve it from memory.When successful, the image causes harmful responses and is queued in the answering agent’s album.
- Textual prompt injection: Textual prompt injection inserts a crafted chat record into an agent’s chat history to persuade agents to generate and spread harmful responses.The textual prompt is designed for multi-agent interaction rather than image-based retrieval.
D.2. Our Infectious Jailbreaking Method
The infectious jailbreak method optimizes an adversarial image for harmful generation and retrieval, validates it with jailbreak and CLIP criteria, and evaluates its spread under multiple attack settings.
- Optimization targets: The adversarial image is optimized using harmful-response, retrieval, and attack-related objectives over sampled multi-agent chat records.The setup first generates clean chat records, samples an ensemble, and combines loss terms for optimization.
- Optimization algorithms: The method uses momentum iterative fast gradient sign optimization with either pixel or border perturbation constraints.The pixel attack bounds the image perturbation, while the border attack restricts changes through a perturbation mask.
- Validation: Validation selects an epoch using jailbreak success rate and minimum CLIP score, with minCLIP prioritized after JSR exceeds 98%.The minimum CLIP score measures whether the adversarial image is retrieved ahead of other album images.
- Computation resource: A one-million-agent, 32-round experiment requires nearly a month on 8×A100 GPUs, showing the computational cost of large-scale evaluation.The experiments use 64 CPU cores and 8×A100 GPUs with 40GB memory each.
E. More Experiments
Additional experiments test infectious jailbreak scalability, larger MLLMs, and alternative harmful-output criteria across infection trajectories and memory settings.
- Scaling Up N to Over One Million: 95.03%, 96.02%, and 96.23% of agents are currently infected at round 22 for N = 2^14, 2^17, and 2^20, respectively.The experiments begin with a virus-carrying ratio of 1/1024 and report almost all agents jailbroken at round 22.
- Ablation settings: Table 4 jointly varies border and pixel attack budgets with text-history and image-album memory sizes.It reports infection ratios at round 16 and the first round when cumulative or current infection reaches 90%.
- Larger MLLMs: The method scales to LLaVA-1.5-13B in addition to the LLaVA-1.5-7B and InstructBLIP-7B models used elsewhere.Figure 20 reports averaged infection curves over five randomly sampled harmful questions and answers, with shaded standard deviations.
- Alternative evaluation criterion: The experiments also evaluate three failure cases using BLEU score as an alternative to exact match for identifying harmful outputs.The alternative criterion is reported through cumulative and current infection ratios over chat rounds.
E.4. More Qualitative Analysis on Failure Cases
The failure analysis quantifies infectious transmission and shows that adversarial outputs can remain harmful even when they do not exactly match the target. Representative cases include substituted or extraneous text, with BLEU scores measuring target similarity.
- Transmission metrics: β_t is computed as the adversarial-image retrieval success rate in the t-th chat round.The measure concerns a questioning agent carrying the adversarial example and passing it to a benign answering agent.
- Transmission metrics: α^Q_t combines adversarial-image retrieval success with jailbreak success for questioning agents.A questioning agent is infected when it retrieves the adversarial example and generates a harmful question.
- Transmission metrics: α^A_t combines retrieval success with jailbreak success for answering agents.The answering-agent condition also requires a virus-carrying questioner that retrieves the adversarial example and receives a harmful answer.
- Qualitative failure cases: Failure cases can preserve harmful content without exactly matching the target, including replaced harmful words and irrelevant appended text.The examples show both altered wording and extra image-related text alongside harmful outputs.
- Additional metrics: 0.83, 0.58, and 0.63 are the average BLEU scores for the three failure cases against their targets.The scores use agent outputs from round 31 and quantify output-target similarity.