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arXiv:2606.29441 · v2

Closing the Activation-Cone Blind Spot: Response-Time Probing and Unified Defense

首次提交 2026-06-28 23:05
版本更新 2026-11-07 12:13
雷达观测 2026-11-10 10:11
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摘要

Note on v2 (Nov 2026): errors found in the evaluation code mean the empirical results reported in v1 are not supported; the theoretical corollary is unaffected. Details are in the notice on page 1 and in KNOWN_ISSUES.md in the linked repository. We compare five inference-time safety paradigms (no defense, static steering, CAST, AlphaSteer, probe-gated steering) across seven instruction-tuned models (7-31B) and five attack types (GCG, AutoDAN, DeepInception, prefilling, intent laundering). We derive a corollary: any defense that gates intervention on a single layer's activation alignment with a benign reference (cone, subspace, or null-space) is blind to attacks that craft activations to lie inside that reference, whether checked at prompt time or per token. For prefilling attacks we propose response-time probing, a linear probe on the hidden state at the first generated tokens combined with a halt, and compose this halt with AlphaSteer's null-space steering. We also examine whether MMLU captures steering's utility cost and how the diversity of negative training sets affects probe false-positive rates. Code, attacks, and per-sample results are released.

作者备注:v2: adds a correction notice; errors in the evaluation code mean the empirical results are not supported (see KNOWN_ISSUES.md). 27 pages, 12 figures, 18 tables. Code and data: https://github.com/bassrehab/response-time-probing

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v2 · 2026-11-07 12:13 · 最新观测版本

Closing the Activation-Cone Blind Spot: Response-Time Probing and Unified Defense

Note on v2 (Nov 2026): errors found in the evaluation code mean the empirical results reported in v1 are not supported; the theoretical corollary is unaffected. Details are in the notice on page 1 and in KNOWN_ISSUES.md in the linked repository. We compare five inference-time safety paradigms (no defense, static steering, CAST, AlphaSteer, probe-gated steering) across seven instruction-tuned models (7-31B) and five attack types (GCG, AutoDAN, DeepInception, prefilling, intent laundering). We derive a corollary: any defense that gates intervention on a single layer's activation alignment with a benign reference (cone, subspace, or null-space) is blind to attacks that craft activations to lie inside that reference, whether checked at prompt time or per token. For prefilling attacks we propose response-time probing, a linear probe on the hidden state at the first generated tokens combined with a halt, and compose this halt with AlphaSteer's null-space steering. We also examine whether MMLU captures steering's utility cost and how the diversity of negative training sets affects probe false-positive rates. Code, attacks, and per-sample results are released.

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