Hujin Peng

Xinle Deng, Ruobin Zhong, Hujin Peng et al.

Memory is essential for enabling large language models to support long-horizon reasoning, yet existing memory systems remain unreliable and difficult to debug. Tracing memory's dynamic evolution is crucial to understand how information is synthesized, propagated, or corrupted over time. In this work, we study the new problem of error tracing and attribution in LLM memory systems. We propose a novel framework that transforms memory pipelines into executable memory evolution graphs, enabling fine-grained tracing of operational information flow. We then construct MemTraceBench, a benchmark collected from representative memory systems such as Long-Context, RAG, Mem0, and EverMemOS, to systematically study memory failure modes. We further introduce an automatic attribution method that iteratively traces operation subgraphs to pinpoint the root cause of any failed case. Our analysis reveals that memory failures are systematic, stemming from operation-level issues like information loss and retrieval misalignment. Crucially, we leverage these fine-grained attribution signals to guide downstream prompt optimization, establishing a closed-loop system that automatically corrects faults and boosts end-task performance by up to 7.62%. Code will be released at https://github.com/zjunlp/MemTrace.

Dezhang Kong, Hujin Peng, Yilun Zhang et al.

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.