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pith:C43N4527

pith:2026:C43N4527I7EBNA4U3P6FEMSGNT
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Content-Aware Attack Detection in LLM Agent Tool-Call Traffic: An Empirical Study of Features, Architectures, and Evaluation Protocols

Sultan Zavrak

MCPShield shows that content embeddings of tool arguments and responses are essential for detecting attacks on LLM agent traffic, lifting AUROC from 0.64 to above 0.89.

arxiv:2605.11053 v3 · 2026-05-11 · cs.CR · cs.AI · cs.LG

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Claims

C1strongest claim

Content-level features are essential: metadata-only detection plateaus around an AUROC of 0.64 regardless of architecture, while content embeddings push the AUROC above 0.89.

C2weakest assumption

The attack examples in RAS-Eval and ATBench are representative of real-world threats and that SBERT embeddings capture generalizable attack signals rather than dataset-specific artifacts.

C3one line summary

MCPShield detects attacks on LLM agent tool-call traffic by encoding sessions as graphs enriched with SBERT content embeddings, achieving AUROC above 0.89 with content features versus 0.64 for metadata alone.

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First computed 2026-05-25T02:01:22.999408Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

1736de775f47c8168394dbfc5232466cc48c2b225a6cac2e82cdcf2700ae03c2

Aliases

arxiv: 2605.11053 · arxiv_version: 2605.11053v3 · doi: 10.48550/arxiv.2605.11053 · pith_short_12: C43N4527I7EB · pith_short_16: C43N4527I7EBNA4U · pith_short_8: C43N4527
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/C43N4527I7EBNA4U3P6FEMSGNT \
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# expect: 1736de775f47c8168394dbfc5232466cc48c2b225a6cac2e82cdcf2700ae03c2
Canonical record JSON
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