T01 · Skill Instruction Hijacking
- Location
references/style-01-坐标蓝图·波普实验室.md:22- Finding
Mandatory Third-Party Branding Injected into Generated Images
- Content
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- Remediation
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Security audit
Security checks for vulnerabilities and agentic risk
This skill mostly does the advertised infographic generation, but it also tells agents to expose a local API key and send generated outputs through an unspecified Feishu script.
Review before installing. This skill should not be allowed to print local API keys or automatically send generated images to Feishu. Only use it with non-sensitive content, require explicit approval before any external delivery, and remove or rewrite the credential-reading and Feishu-sending instructions before trusting it in an agent workflow.
references/style-01-坐标蓝图·波普实验室.md:22Mandatory Third-Party Branding Injected into Generated Images
README.md:49API Key Extracted from OpenClaw Configuration and Printed to Standard Output
SKILL.md:47Generated Content Is Directed to an Unreviewed External Feishu Delivery Script
The trigger condition is broad enough to match common requests about articles, notes, visualizations, or knowledge graphics, which can cause the skill to activate unexpectedly. Overbroad activation increases the chance that operators or agents will run embedded operational steps, including image generation and related side effects, in situations where the user did not explicitly request this specific workflow.
The document includes operational instructions to execute image-generation commands, access credentials, and send outputs externally, but provides no user-consent, data-handling, or secret-safety warnings. In context, this is dangerous because the skill processes user-supplied article content and may transmit it to third-party services or messaging endpoints without making those side effects explicit.
The README explicitly instructs operators to read a provider API key from a local configuration file using a shell pipeline. That expands the skill’s capabilities from image generation into credential access, which is unnecessary for the stated user-facing purpose and creates a concrete risk of secret exposure, reuse in other contexts, or normalization of secret-handling by untrusted skill content.
The trigger description is broad enough that the skill could activate for many content-creation requests without a clear boundary on when it should be used. Over-broad invocation can cause unintended processing of user content and can chain into the documented outbound-sharing behavior, making accidental data exposure more likely.
The usage example tells the operator to combine the user’s article with a prompt and generate an unspecified number of images, but it does not define scope, limits, or privacy expectations. Ambiguous invocation patterns increase the chance of over-processing or invoking the skill in contexts the user did not clearly intend.
The skill’s documented behavior expands from image generation into outbound delivery via send_to_feishu.sh, which is a separate data-transmission capability. That creates an unexpected exfiltration path for user-provided content and generated outputs, especially since the manifest does not declare or justify external sharing.
Sending outputs through Feishu is not necessary for the stated purpose of generating high-density images and therefore violates least functionality. Unnecessary external delivery increases privacy and data-handling risk because generated images may embed sensitive user content from the source article.
The instruction to send generated images via a shell script introduces data transmission to a third-party channel without any user-facing disclosure or confirmation. Because the generated image is derived from user-supplied article content, this can leak sensitive or proprietary information outside the expected execution environment.
The manifest describes a skill for generating high-density visual images from user-provided article/content. In this file, the documented workflow adds capabilities to search Xiaohongshu high-performing posts and call a knowledge base, which goes beyond straightforward content-to-image generation and is not declared in the skill description.
The workflow explicitly requires searching Xiaohongshu high-like-count notes before generating output, which expands the skill beyond image creation into external platform research without clear user consent or necessity. In an agent setting, this can trigger unintended browsing, collection of third-party content, and policy/scope violations, especially if the runtime has network access.
The instruction 'All text in CHINESE' is a natural-language locale constraint that applies categorically to generated output. The file does not offer an opt-in, alternative language option, or a clear justification that this skill is restricted to a Chinese-only compliance or regional context.
The markdown instructs the agent to 'Include small English subtitles below main headings,' which imposes a specific language choice as part of the output format. The file does not provide an opt-in, alternative language option, or justification for why English is required.
The skill instructs the agent to search internet platforms and gather external material, which expands behavior beyond the declared role of transforming user-provided content into infographic output. This creates scope creep: the agent may fetch unverified external content, introduce privacy/compliance issues, or act with capabilities the user did not explicitly authorize.
The content template explicitly says to add an English subtitle for every image, creating a fixed language policy in the generated output. Because no user choice or locale justification is provided, this conflicts with the language/locale policy guidance.
Line L58 requires a 'Specific English prompt' for the generated visual prompt. This imposes a language constraint without any opt-in or alternative, which matches the language/locale policy violation criteria.
The heading explicitly labels this as an English MD version, which indicates a fixed language requirement. Because the file does not provide an opt-in or alternative language path, it may violate locale/language choice policy for users who did not request English.
The template requires an "English Midjourney/DALL-E prompt," which forces a specific language in generated content. The file does not mention user preference, fallback behavior, or a justified regional constraint.
文件的描述和使用方式均以中文固定表述技能行为,没有说明是否支持其他语言或允许用户自行选择输出语言。若组织要求避免未经用户同意强制特定语言,这种单一语言约束属于自然语言层面的策略风险。
This file presents the skill heading in Chinese while the operative instructions are written in English, but nowhere tells the user they may choose their preferred language or locale. Under the language/locale policy rule, imposing a language convention without explicit user choice can be a natural-language policy issue.
The skill content defines the role and workflow entirely in English despite the file title being Chinese, and it does not state that the user may choose the output language. This can create a language-policy issue if organizational guidance requires respecting user locale or offering opt-in for a specific language.
The markdown prompt explicitly requires '中文标签用手写风格字体', which imposes a specific language choice. Under the policy criteria, forcing a language or locale without offering user choice or documenting a justified regional constraint is a natural-language policy violation.
No suspicious patterns detected.