Web Fraud Attacks Against LLM-Driven Multi-Agent Systems (2509.01211v1)

Abstract: With the proliferation of applications built upon LLM-driven multi-agent systems (MAS), the security of Web links has become a critical concern in ensuring system reliability. Once an agent is induced to visit a malicious website, attackers can use it as a springboard to conduct diverse subsequent attacks, which will drastically expand the attack surface. In this paper, we propose Web Fraud Attacks, a novel type of attack aiming at inducing MAS to visit malicious websites. We design 11 representative attack variants that encompass domain name tampering (homoglyph deception, character substitution, etc.), link structure camouflage (sub-directory nesting, sub-domain grafting, parameter obfuscation, etc.), and other deceptive techniques tailored to exploit MAS's vulnerabilities in link validation. Through extensive experiments on these crafted attack vectors, we demonstrate that Web fraud attacks not only exhibit significant destructive potential across different MAS architectures but also possess a distinct advantage in evasion: they circumvent the need for complex input formats such as jailbreaking, which inherently carry higher exposure risks. These results underscore the importance of addressing Web fraud attacks in LLM-driven MAS, as their stealthiness and destructiveness pose non-negligible threats to system security and user safety.

• The paper provides a novel categorization of web fraud attacks that exploit vulnerabilities in link validation of LLM-driven multi-agent systems.
• It introduces 11 distinct attack vectors, including domain tampering and parameter obfuscation, achieving up to 93.6% success in bypassing defenses.
• The study underscores the urgent need for adaptive, interdisciplinary defense mechanisms to secure AI-powered web interactions.

Web Fraud Attacks Against LLM-Driven Multi-Agent Systems

The paper "Web Fraud Attacks Against LLM-Driven Multi-Agent Systems" (2509.01211) introduces a novel class of security threat termed as Web fraud attacks. These attacks are specifically crafted to exploit the vulnerabilities in LLM-driven multi-agent systems (MAS), which are increasingly used in various AI applications involving dynamic interaction with web resources. This essay provides an expert summary of the key findings, methodologies, and implications from this research.

Introduction to Web Fraud Attacks

The authors identify a critical vulnerability in MAS concerning their ability to accurately validate and process web links. MAS are being widely adopted due to their capability to perform tasks requiring complex reasoning, multimodal content generation, and real-time data processing. However, these systems are vulnerable to Web fraud attacks, which involve inducing MAS to mistakenly identify malicious websites as benign.

With 11 distinct attack vectors developed, the researchers categorized these into groups such as domain name tampering, link structure camouflage, and parameter obfuscation. The presented attacks demonstrate significant success in bypassing existing defense mechanisms established in MAS environments, thereby emphasizing a crucial security loophole.

Figure 1: Examples of Web fraud attacks.

Attack Variants and Mechanisms

The Web fraud attacks are characterized by their ability to conceal malicious intents through structural manipulation of web links. These include techniques like IP obfuscation, domain name manipulation, and strategic use of homoglyphs, which are used to fool MAS into misclassifying the nature of the web links.

The core ingenuity of these attacks lies in their exploitation of an MAS's natural blind spots in link parsing. For instance, sophisticated attacks like homograph and subdomain imitation utilize deceptive visual similarities in URLs to mislead unsophisticated MAS verifications. The parameter manipulation attack further underscores the precision required to protect MAS from these camouflaged threats.

Figure 2: Illustration of a Web link.

Experimental Evaluation

An extensive evaluation of these attacks was conducted across several MAS platforms, models, and architectures. The experiments revealed alarming success rates, demonstrating that existing system defenses are largely inadequate against these newly unveiled attack vectors. Notably, the attacks exhibit resilience across varied MAS configurations, achieving penetration without complex jailbreak techniques.

Success metrics showed that 93.6% success rates were achieved on robust architectures like "Review," indicating structural weaknesses that can be exploited. Moreover, both platform and defense strategy configurations showed poor resistance, underscoring the attacks' ability to adapt and penetrate diverse system defenses.

Figure 3: Four MAS architectures used in experiments.

Variability Across System Components

The impact of attack vectors was shown to vary significantly with different models and platforms, with certain architectures like "Debate" offering more resilience due to their internal verification processes. This variational success underscores the necessity for tailored defense mechanisms.

Evaluations demonstrated systemic vulnerabilities when agents were altered subtly to deceive security checks. These findings suggest a fundamental gap in the semantic understanding capabilities of LLMs when parsing more intricate web link structures.

Figure 4: Attack results varying with architectures (platform: MetaGPT, model: GPT-4o-mini).

Implications and Future Work

The implications of Web fraud attacks extend beyond immediate security threats, suggesting future pathways for enhancing MAS defenses. One critical realization is the need for specialized, context-aware defense mechanisms that can dynamically adapt to evolving threat landscapes.

Furthermore, the research calls for an interdisciplinary approach in addressing such vulnerabilities, combining advances in secure computation, web semantics, and AI-driven heuristic checks to fortify MAS against multifaceted cyber-attacks.

Figure 5: Attack results varying with models (platform: MetaGPT, architecture: Review).

Conclusion

Web fraud attacks present a nuanced addition to the field of cybersecurity, particularly in the context of LLM-driven MAS. By highlighting the shortcomings of current MAS defenses against sophisticated fraud techniques, the paper emphasizes a pressing need for dedicated research and robust engineering practices. Future research must prioritize these challenges to innovate protection mechanisms, ensuring that MAS applications can safely transform how AI interacts with the web.

Figure 6: Results varying with different platforms (architecture: Linear, model: GPT-4o-mini).