Measuring and Exploiting Implicit Trust in LLM Tool-Calling Pipelines
In this paper, we present a framework to measure the trust profile of an arbitrary LLM based on a variety of payload framings sent through different channels. Following this assessment, we devise cross-channel fragmentation attacks that distribute seemingly benign payloads across two or three channels; no individual channel carries a complete injection, yet the LLM compiles the fragments into credential exfiltration. We evaluated our attacks across 12 frontier models, three production clients, and six payloads, totalling over 15,000 trials.
- ▪In this paper, we present a framework to measure the trust profile of an arbitrary LLM based on a variety of payload framings sent through different channels.
- ▪Following this assessment, we devise cross-channel fragmentation attacks that distribute seemingly benign payloads across two or three channels; no individual channel carries a complete injection, yet the LLM compiles the fragments into cre
- ▪We evaluated our attacks across 12 frontier models, three production clients, and six payloads, totalling over 15,000 trials.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2609.18217 |
| Publication time | Thu, 17 Sep 2026 08:07:08 +0000 |
| Retrieval time | 2026-09-17T08:18:42.519Z |
| Last seen | 2026-09-17T08:18:42.519Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Ktsjvhl5a1tA · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Cryptography and Security arXiv:2609.18217 (cs) [Submitted on 16 Sep 2026] Title:Measuring and Exploiting Implicit Trust in LLM Tool-Calling Pipelines Authors:Murali Ediga, Sudipta Chattopadhyay View a PDF of the paper titled Measuring and Exploiting Implicit Trust in LLM Tool-Calling Pipelines, by Murali Ediga and Sudipta Chattopadhyay View PDF HTML (experimental) Abstract:The Model Context Protocol (MCP) enables LLMs to invoke external tools, but every tool interaction exposes the model to attacker-controlled text through multiple input channels (tool descriptions, tool results, sampling messages) that share a single context window without privilege separation.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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