The Envelope Layer: An Underestimated Surface of LLM Agent Defense

Prior work framed the envelope layer as the channel through which malicious content enters an agent, and established it as a defense surface independent of the LLM layer and the agent layer; however, that work only tested the envelope layer briefly and did not examine how label strength, envelope authenticity, scaffold differences, or existing memories affect defense effectiveness. Following its GDA Measurement methodology and the A/B/C/D outcome taxonomy, this paper takes the A2A-TIBA attack as the test vehicle, uses the semantic breach rate as the primary indicator with the semantic refusal rate as its complementary reference, and characterizes the security boundary of the envelope layer through a two-by-two factorial experiment and a memory experiment. The results show that: when the envelope is authentic, strong labels consistently reduce the semantic breach rate, whereas weak labels are not necessarily better than an unlabeled baseline; when the envelope can be forged, the benefit of strong labels is weakened and its direction varies across frontends; and memory has a larger effect than any label manipulation: success memory raises the semantic breach rate while failure memory lowers it, and most combinations that contain both experiences return to the no-memory baseline. Accordingly, this paper refines the envelope layer from a single admission channel into two levels, an admission channel plus a context assembly surface, and argues that its security boundary is not a fixed property of labels but a dynamic outcome jointly shaped by label claims, frontend permissions, and historical memory.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-10-27
DOI
https://doi.org/10.48550/arxiv.2610.37415
Primary Topic
Adversarial Robustness in Machine Learning
Type
preprint
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preprint

The Envelope Layer: An Underestimated Surface of LLM Agent Defense

Yuelin Han
arXiv (Cornell University)
Adversarial Robustness in Machine Learning
preprint

The Envelope Layer: An Underestimated Surface of LLM Agent Defense

Yuelin Han
preprint en

Abstract

Prior work framed the envelope layer as the channel through which malicious content enters an agent, and established it as a defense surface independent of the LLM layer and the agent layer; however, that work only tested the envelope layer briefly and did not examine how label strength, envelope authenticity, scaffold differences, or existing memories affect defense effectiveness. Following its GDA Measurement methodology and the A/B/C/D outcome taxonomy, this paper takes the A2A-TIBA attack as the test vehicle, uses the semantic breach rate as the primary indicator with the semantic refusal rate as its complementary reference, and characterizes the security boundary of the envelope layer through a two-by-two factorial experiment and a memory experiment. The results show that: when the envelope is authentic, strong labels consistently reduce the semantic breach rate, whereas weak labels are not necessarily better than an unlabeled baseline; when the envelope can be forged, the benefit of strong labels is weakened and its direction varies across frontends; and memory has a larger effect than any label manipulation: success memory raises the semantic breach rate while failure memory lowers it, and most combinations that contain both experiences return to the no-memory baseline. Accordingly, this paper refines the envelope layer from a single admission channel into two levels, an admission channel plus a context assembly surface, and argues that its security boundary is not a fixed property of labels but a dynamic outcome jointly shaped by label claims, frontend permissions, and historical memory.

arXiv (Cornell University)
Adversarial Robustness in Machine Learning
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