Xiao, Charles · Nov 11, 2026
Charles Xiao: When Safety Becomes an Attack Surface
Markdown · JSON · Original source
Charles Xiao argues that an ’s refusal to act can be exploited: fabricated risk warnings placed in low-trust content may stop the agent from completing legitimate tasks. He calls this warning-induced over-abstention an availability attack. His central point is that safe abstention depends not only on what a warning claims, but also on whether its evidence comes from an authoritative source. Xiao tests a Verify-Before-Abstain protocol, which checks warnings against authoritative records or the underlying content. Across three model families and 51 injectable AgentAbstain task pairs, caution-first prompting increased induced over-abstention for Gemini from 9.8% to 86.3%, while clean-task completion remained unchanged. The abstract also reports that warning wording affected results across models and environments, and that provenance-aware verification improved legitimate task completion. But forgery and sensitive-content controls exposed limits: verification depends on authoritative evidence and may not preserve confidentiality. The paper’s argument is that safety evaluation should examine why an agent refrains from acting, as well as the safety of its actions.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Nov 11, 2026 (2026-11-11).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-13T15:32:41.837Z. First observed: 2026-11-13T07:39:34.240Z. Retrieved: 2026-11-13T15:05:06.723Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The supplied evidence is the paper’s abstract; it does not include the full methods, task details, or results needed to assess how broadly the findings apply. The abstract reports limitations involving confidentiality and dependence on authoritative evidence, but does not specify their detailed conditions or extent.
Open question
How well does Verify-Before-Abstain preserve confidentiality when authoritative evidence is unavailable or the warning involves sensitive content?
Source-stated
Charles Xiao argues that fabricated warnings in low-trust content can prevent AI agents from completing legitimate tasks, making abstention an availability attack.
adversaries place fabricated risk warnings in low-trust content to prevent legitimate task completion
Source-stated
The paper presents safe abstention as requiring evaluation of both a warning’s claimed risk and the authority of its supporting evidence.
safe abstention requires evaluating both the claimed risk and the authority of the evidence supporting it
Source-stated
The authors evaluate Verify-Before-Abstain, which checks warnings against authoritative records or underlying content.
corroborates warnings against authoritative records or the underlying content
Source-stated
Across the reported task pairs, caution-first prompting raised induced over-abstention for Gemini from 9.8% to 86.3%, while clean-task completion was unchanged.
For Gemini, induced over-abstention rises from $9.8\%$ to $86.3\%$, despite unchanged clean-task completion.
Source-stated
The abstract reports that provenance-aware verification improved legitimate completion, but that its dependence on authoritative evidence and its limits in preserving confidentiality were exposed by controls.
Provenance-aware verification improves legitimate completion, while forgery and sensitive-content controls expose its dependence on authoritative evidence and its limitations in preserving confidentiality.
Source-stated
The authors argue that agent safety evaluations should consider the justification for restraint as well as the safety of actions taken.
These results motivate evaluating agent safety through the justification for restraint, alongside the safety of actions taken.
Source and contributor context
Xiao, Charles · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
AI agent
An AI system that chooses and carries out actions toward a goal, often using software tools.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: db78470e-ec6e-4023-8dcc-e3258884ac18.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-13T15:32:41.837Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 121dfdcdcb857a75cae22a36ed73646435486f0a1940bb12dbb6c2280e24615e. Source text hash: a45021351579f37bfb104a0c1b39bf641ac22d81572726d65a74991296387e12.
Revision and source-check history
Version 1 · 2026-11-13T15:32:41.837Z
Charles Xiao: When Safety Becomes an Attack Surface
Charles Xiao argues that an ’s refusal to act can be exploited: fabricated risk warnings placed in low-trust content may stop the agent from completing legitimate tasks. He calls this warning-induced over-abstention an availability attack. His central point is that safe abstention depends not only on what a warning claims, but also on whether its evidence comes from an authoritative source. Xiao tests a Verify-Before-Abstain protocol, which checks warnings against authoritative records or the underlying content. Across three model families and 51 injectable AgentAbstain task pairs, caution-first prompting increased induced over-abstention for Gemini from 9.8% to 86.3%, while clean-task completion remained unchanged. The abstract also reports that warning wording affected results across models and environments, and that provenance-aware verification improved legitimate task completion. But forgery and sensitive-content controls exposed limits: verification depends on authoritative evidence and may not preserve confidentiality. The paper’s argument is that safety evaluation should examine why an agent refrains from acting, as well as the safety of its actions.
Original source
cs.AI · Article
Source details
Original source date: Nov 11, 2026 (2026-11-11).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-13T15:32:41.837Z. First observed: 2026-11-13T07:39:34.240Z. Retrieved: 2026-11-13T15:05:06.723Z.
Limits
The supplied evidence is the paper’s abstract; it does not include the full methods, task details, or results needed to assess how broadly the findings apply. The abstract reports limitations involving confidentiality and dependence on authoritative evidence, but does not specify their detailed conditions or extent.
Open question
How well does Verify-Before-Abstain preserve confidentiality when authoritative evidence is unavailable or the warning involves sensitive content?
Source-stated
Charles Xiao argues that fabricated warnings in low-trust content can prevent AI agents from completing legitimate tasks, making abstention an availability attack.
adversaries place fabricated risk warnings in low-trust content to prevent legitimate task completion
Source-stated
The paper presents safe abstention as requiring evaluation of both a warning’s claimed risk and the authority of its supporting evidence.
safe abstention requires evaluating both the claimed risk and the authority of the evidence supporting it
Source-stated
The authors evaluate Verify-Before-Abstain, which checks warnings against authoritative records or underlying content.
corroborates warnings against authoritative records or the underlying content
Source-stated
Across the reported task pairs, caution-first prompting raised induced over-abstention for Gemini from 9.8% to 86.3%, while clean-task completion was unchanged.
For Gemini, induced over-abstention rises from $9.8\%$ to $86.3\%$, despite unchanged clean-task completion.
Source-stated
The abstract reports that provenance-aware verification improved legitimate completion, but that its dependence on authoritative evidence and its limits in preserving confidentiality were exposed by controls.
Provenance-aware verification improves legitimate completion, while forgery and sensitive-content controls expose its dependence on authoritative evidence and its limitations in preserving confidentiality.
Source-stated
The authors argue that agent safety evaluations should consider the justification for restraint as well as the safety of actions taken.
These results motivate evaluating agent safety through the justification for restraint, alongside the safety of actions taken.
Source and contributor context
Xiao, Charles · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
AI agent
An AI system that chooses and carries out actions toward a goal, often using software tools.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: db78470e-ec6e-4023-8dcc-e3258884ac18.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-13T15:32:41.837Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 121dfdcdcb857a75cae22a36ed73646435486f0a1940bb12dbb6c2280e24615e. Source text hash: a45021351579f37bfb104a0c1b39bf641ac22d81572726d65a74991296387e12.
passed · 2026-11-13T15:32:41.837Z · anthropic/claude-opus-5.5
Publication: db78470e-ec6e-4023-8dcc-e3258884ac18
attribution — pass: The paper is correctly attributed to Charles Xiao, the sole listed author. Its arguments and results are framed as source claims. Some claims say 'the authors,' echoing the abstract's 'we,' but this does not misattribute the work.
Authors:Charles Xiao
summary — pass: The summary faithfully conveys the thesis, the protocol, the Gemini result, the effect of warning wording, and the stated limits. It preserves the closing argument about evaluating why an agent holds back. Abstention is explained as refusal to act, and the availability attack is explained through its effect of blocking legitimate tasks. Terms like 'caution-first prompting' and 'clean-task completion' could use tooltips, but they do not block understanding. The question follows from the stated confidentiality limit.
An AI agent's decision not to act can itself become an attack surface.
date — pass: The originalPublished date of 2026-11-11 matches the arXiv v1 submission date. It is not the later feed date of November 13.
[Submitted on 11 Nov 2026]
support — pass: Each claim matches an exact abstract excerpt, including the 9.8% to 86.3% Gemini figures and the unchanged clean-task completion. The protocol's benefits are stated with the source's own caveats about forgery, sensitive content, and confidentiality. The reading does not overstate results as real-world reliability, and its limitations correctly note that only the abstract was available.
For Gemini, induced over-abstention rises from $9.8\%$ to $86.3\%$, despite unchanged clean-task completion.
media — pass: The selected media is the verified direct link to the arXiv abstract page, which is the original source. No better source media was found.
Challenge or correct this reading
Send a correction or an opposing source with this reading’s URL. Disagreement and uncertainty can remain unresolved.
An AI system that chooses and carries out actions toward a goal, often using software tools.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivAn AI system that chooses and carries out actions toward a goal, often using software tools.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXiv