WebWeaver: Breaking Topology Confidentiality in LLM Multi-Agent Systems with Ste

On March 20th, a research team posted a preprint introducing WebWeaver, a framework built around a disquieting premise: that LLM multi-agent systems may be leaking their own architectural blueprints simply by doing their jobs. The technique they describe — stealthy context-based inference — attempts to reconstruct the hidden topology of a multi-agent network from the outside, without credentials, without access to system configurations, and without triggering the kind of alarms that conventional probes would set off. The attack vector is the context itself. As agents communicate, coordinate, and pass instructions through their normal workflows, patterns accumulate. WebWeaver, as the researchers describe it, reads those patterns the way a linguist reads an accent — inferring structure from the texture of ordinary output. The paper appears to be sitting quietly in the preprint stack, one signal tracked, no secondary coverage yet.

If confirmed, here is what this means. Every organisation currently running multi-agent LLM pipelines under the assumption that topology confidentiality provides meaningful security needs to reconsider that assumption today. The threat is not brute force — it is observational. Knowing the architecture of a system is not just academic; it tells an adversary where the bottlenecks are, which agents hold elevated trust, and where to focus prompt injection or poisoning efforts for maximum effect. The second-order implication is arguably worse: if topology can be inferred passively from normal operation, then the standard defence of simply hiding configuration details offers far less protection than the field has assumed. Multi-agent orchestration frameworks — AutoGen, CrewAI, LangGraph and their successors — were not designed with this attack surface in mind. Security audits that have not stress-tested against context-based topology inference are now, at best, incomplete.

Watch for independent replication attempts or a response from any of the major multi-agent framework teams — either would sharply change the weight this story deserves.

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