Ae4
Medium
- Category
- analysis-evasion
- Confidence
- 80% confidence
- Finding
Suspicious Unicode normalization or mixed-script content
- Content
Security audit
08 Data Growth
Security checks for vulnerabilities and agentic risk
Overview
This is a Chinese analytics guide for community group-buying growth with local examples and no hidden execution, persistence, or data exfiltration behavior, though users should verify privacy compliance and the sample-size calculator.
Installers should treat this as a business analytics reference, not an automated data pipeline. Before applying its segmentation, geofencing, or cross-platform data ideas, ensure customer data use is authorized, minimized, and legally compliant; also validate the included A/B sample-size calculator against a trusted statistics library.
Vulnerability Patterns
- Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
- Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
- Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review
- Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally
- Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs
Vulnerability Patterns
- Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
- MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
- Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
- Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
- Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
Findings (6)
Missing User Warnings
Medium
- Category
- Not specified by scanner
- Confidence
- 90% confidence
- Finding
This section recommends extensive user profiling, cross-platform/ecosystem data use, behavior tagging, geofencing, and segmentation strategies without any privacy, consent, retention, minimization, or legal-compliance guidance. In practice, adopters could implement invasive tracking or combine datasets improperly, creating privacy violations, regulatory exposure, and misuse of sensitive behavioral data.
- Content
Ae4
Medium
- Category
- analysis-evasion
- Confidence
- 80% confidence
- Finding
Suspicious Unicode normalization or mixed-script content
- Content
Natural-Language Policy Violations
Medium
- Category
- Not specified by scanner
- Confidence
- 95% confidence
- Finding
SQP-3 applies to all file types and includes language or locale policy violations. The introductory description and subsequent tool content are presented only in Chinese, with no indication that users can opt into another language or that the skill is intended solely for a Chinese-language audience.
- Content
Intent-Code Divergence
Medium
- Category
- Not specified by scanner
- Confidence
- 94% confidence
- Finding
The sample-size calculator claims to use a two-sided proportion test, but
get_z_value(1 - alpha / 2)is incompatible with the lookup table semantics, causing the wrong critical value to be used. This can materially under- or over-estimate required sample size, leading operators to ship decisions based on underpowered experiments or wasted traffic, which is a real integrity risk in a data-driven decision tool.- Content
Natural-Language Policy Violations
Low
- Category
- Not specified by scanner
- Confidence
- 95% confidence
- Finding
The natural-language instructions and descriptions are consistently Chinese-only, and there is no indication that users may choose another language or that the locale constraint is intentional and justified. This can violate language/locale policy when a skill effectively forces one language without user opt-in.
- Content
Static analysis
No suspicious patterns detected.