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When Skills Lie: Hidden-Comment Injection in LLM Agents

Qianli Wang, Boyang Ma, Minghui Xu, Yue Zhang

arXiv:2602.10498v1cs.CR

TL;DR

The paper studies whether hidden HTML comments in Skills can inject instructions that redirect LLM agents during benign tasks. It tests this risk in tool-planning settings and finds that both evaluated models produced sensitive tool intentions, while a short defense prompt prevented them and surfaced the suspicious content.

  • Problem

    The paper addresses the risk that hidden comments in Skills remain available to LLMs while being invisible to human reviewers, creating a prompt-injection blind spot.

    Source: §1 · p. 1, §3 · p. 2, §4 · p. 2

  • Method

    The study models attackers appending hidden HTML instructions to otherwise clean Skills and evaluates whether agents propose malicious tools from the resulting context.

    Source: §4 · p. 2

  • Results

    DeepSeek-V3.2 and GLM-4.5-Air produced sensitive tool intentions under hidden-comment injection, while a defensive prompt stopped malicious tool proposals and surfaced suspicious instructions.

    Source: §5.2 · p. 3, §6 · p. 4

  • Takeaways & Limitations

    Treating Skills as untrusted input at the prompt boundary can reduce unsafe tool planning while preserving the user-facing task.

    Source: §5.3 · p. 3, §6 · p. 4

    The attacker cannot change model weights or the tooling backend and is limited to inserting hidden text into the Skill document.

    Source: §4 · p. 2

Abstract

from arXiv · show

LLM agents often rely on Skills to describe available tools and recommended procedures. We study a hidden-comment prompt injection risk in this documentation layer: when a Markdown Skill is rendered to HTML, HTML comment blocks can become invisible to human reviewers, yet the raw text may still be supplied verbatim to the model. In experiments, we find that DeepSeek-V3.2 and GLM-4.5-Air can be influenced by malicious instructions embedded in a hidden comment appended to an otherwise legitimate Skill, yielding outputs that contain sensitive tool intentions. A short defensive system prompt that treats Skills as untrusted and forbids sensitive actions prevents these malicious tool calls and instead surfaces the suspicious hidden instructions.

1 Introduction

Skills improve LLM-agent usability by packaging tool guidance into reusable procedures, but hidden HTML comments can conceal malicious instructions from reviewers while remaining visible to the model. The study finds that such injections can influence sensitive tool intentions, whereas a defensive prompt exposes and blocks them.

  • Skills reduce user effort by encapsulating complex tool-use prompts into reusable procedures, but they also expand the agent’s attack surface.Source: §1 · p. 1
  • Hidden HTML comments can conceal malicious instructions from human reviewers while raw Skill text remains in the model context.Source: §1 · p. 1
  • Hidden-comment instructions appended to a legitimate Skill influenced LLMs toward sensitive tool intentions despite benign user requests.Source: §1 · p. 1
  • A defensive system prompt forbidding sensitive actions prevented malicious tool calls and surfaced the suspicious hidden content.Source: §1 · p. 1

2 Background

Skills function as high-priority prompt components that guide tool selection and planning. HTML comments create a visibility gap: users reviewing rendered documentation may miss instructions that remain fully exposed in the model’s raw context.

2.1 Skills In LLM Agents

  • Skills are reusable documentation that specifies available tools and recommended procedures for LLM agents.Source: §2.1 · p. 1
  • LLM agents treat raw Skill files as executable prompt components that define tool-use boundaries and guide decision-making.Source: §2.1 · p. 1

2.2 Hidden-Instruction Prompt Injection

  • HTML rendering hides comment-enclosed payloads from human reviewers while leaving them fully exposed to LLMs in the raw Skill text.Source: §2.2 · p. 2
  • Prompt injection exploits models’ tendency to follow authoritative-looking instructions, making the Skill visibility gap a practical attack carrier.Source: §2.2 · p. 2

3 Motivation

The paper examines whether hidden comments in otherwise clean Skills can redirect tool planning during benign tasks. It specifically asks whether a short defense prompt can block this effect without disrupting the requested workflow.

  • The visibility mismatch between rendered Skills and raw model context can shift agents from benign workflows toward sensitive actions.Source: §3 · p. 2
  • The study tests whether an appended hidden comment changes proposed or called tools and whether a short defense prompt blocks the effect.Source: §3 · p. 2
  • In the formatting example, a compromised Skill can steer the agent toward environment inspection or credential-file access before formatting begins.Source: §3 · p. 2

4 Threat Model

The threat model targets agents that ingest Skills into context and emit structured tool calls, with attackers modifying only hidden Skill text. Success is defined by malicious tool names appearing in output metadata, even without execution.

  • The model is assumed to read Skills and respond to user requests by planning and emitting structured tool calls.Source: §4 · p. 2
  • Attackers append hidden HTML instructions to a clean Skill to alter action planning without changing its visible content.Source: §4 · p. 2
  • The attack surface is the prompt-construction pipeline that ingests Skill documents verbatim into the model context.Source: §4 · p. 2, Figure 1 · p. 2
  • Attack success is any tool-call metadata containing list_environment_variables, read_file, or http_request, even when no action executes.Source: §4 · p. 2

5 Experiments

The experiments test whether hidden HTML-comment instructions in Skills influence tool planning and whether defenses prevent unsafe proposals. Results show malicious tool intent under injection, while defensive mechanisms restore safe behavior and motivate design safeguards.

5.1 Experimental Setup

  • The setup used a benign formatting request, clean versus malicious Skills, and a hidden comment directing environment enumeration, credential-file reads, and outbound data transmission.The defense combined an untrusted-Skill prompt guardrail with execution-layer restrictions on sensitive access.Source: §5.1 · pp. 2–3

5.2 Result

  • Both evaluated models produced malicious tool intent under hidden-comment injection, whereas the defended condition eliminated malicious proposals and surfaced the suspicious instructions.The attack-success criterion concerns mentions of list_environment_variables, read_file, or http_request in tool-call text.Source: §5.2 · p. 3, Table 1 · p. 3
  • The injected tools shift the workflow from code formatting toward access to environment variables, local credentials, and outbound HTTP channels that can support exfiltration.Even without execution, proposing these tools can be interpreted as intent and violate security boundaries.Source: §5.2 · p. 3

5.3 Implications for Skill Design

  • Skills should expose model-facing text for review or strip hidden content before context insertion, aligning human-visible documentation with what models receive.The recommendation addresses the rendered-versus-raw visibility gap.Source: §5.3 · p. 3
  • Because review is fast and partial, systems should highlight unusual instructions and sensitive tool references instead of assuming careful manual inspection.This implication is framed as designing for bounded attention in IDE workflows.Source: §5.3 · p. 3
  • Skills should present procedures as guidance subordinate to system safety rules, not as a higher-priority policy channel.The rationale is that imperative language can influence LLM behavior.Source: §5.3 · pp. 3–4
  • Agents should surface suspicious hidden instructions, explain when they ignore them, and provide transparent refusal reasoning for sensitive actions.This supports human oversight and recovery when users cannot infer the agent’s internal plan from a polite response.Source: §5.3 · p. 4

6 Conclusion

The paper shows that hidden comments appended to legitimate Skills can steer agents toward sensitive actions despite benign requests. Treating Skills as untrusted at the prompt boundary can restore transparency and reduce unsafe tool planning.

  • Hidden-comment injection can shift LLMs toward proposing sensitive actions even when the user’s request is benign.The targeted assets include environment variables, local credential files, and network egress channels.Source: §6 · p. 4
  • For DeepSeek-V3.2 and GLM-4.5-Air, a simple defense prompt prevented malicious tool outputs and surfaced suspicious hidden instructions.The defense treats Skills as untrusted and forbids sensitive actions.Source: §6 · p. 4
  • The rendered-versus-raw visibility gap undermines human review and accountability because users may authorize a clean-looking Skill while models receive hidden text.The paper frames Skills as cognitive artifacts that shape trust and attention.Source: §6 · p. 4
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