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Security audit

Self-Improving Agent

Security checks for vulnerabilities and agentic risk

Overview

This skill is a memory and self-improvement framework, but it broadly stores, reuses, and applies long-term user history without enough consent or scoping controls.

Install only if you intentionally want a broad, persistent memory layer. Before use, require explicit approval for L2/L3 writes, disable global automatic memory injection by default, keep memories scoped to a user/project, exclude secrets and sensitive personal data, and review any generated or updated skills before registration.

Vulnerability Patterns
  • Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
  • Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
  • 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
Findings (1)

T02 · Agent Memory Poisoning

Error
Location
SKILL.md:141
Finding

Persistent Memory Poisoning Through Untrusted User-Derived Rules

Content
View full analysis
inspect L3 long-term memory 2. memory_search(error type/tool name) -> inspect historical failures 3. Read today's and yesterday's memory log files When an L3 experience matches: - Directly apply the verified solution and skip the trial-and-error stage. When the current problem is more than 70% similar to a historical record: 1. Inform the user that this problem occurred previously. 2. Directly apply the historical solution and verify whether it remains valid. 3. If the historical solution fails, update the recorded experience. ``` `SKILL.md:271-276` enables automatic cross-session propagation: ```text Pro mode: - Complete three-layer persistent memory across sessions - Cross-conversation global reuse: L3 experiences are automatically injected into the context of every new conversation - Automatic extraction of recurring patterns into independent Skills ``` `memory-schema.md:109-128` defines persistent user-profile entries and unrestricted memory writes: ```text Long-term user profile entry: Preference: {preference category} - Content: specific preference description - Strength: strong / medium / weak - Scope: global / specific project / specific tool Write protocol: Write L1: maintain directly in the current context Write L2: add content to the daily memory target Write L3: add content to the permanent memory target ``` `skill-extraction.md:35-41` allows repeated user corrections to become persistent behavioral instructions: ```text Rule 3: User preference pattern detection If the ...[truncated 3887 chars]
Remediation
View remediation
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
Findings (16)

Vague Triggers

High
Category
Not specified by scanner
Confidence
97% confidence
Finding

The skill declares itself '全场景通用' and uses very broad trigger phrases such as memory, optimization, mistakes, and past experience, which can cause unintended activation in many unrelated conversations. Because the skill also enables persistent memory behaviors and automatic workflow changes, accidental invocation can expand data collection and action scope without clear user intent.

Content

No source excerpt is available for this finding.

Missing User Warnings

High
Category
Not specified by scanner
Confidence
97% confidence
Finding

The L3 design explicitly stores user profiles, preferences, work habits, technical decisions, and reusable knowledge permanently across sessions, but the skill does not require informed consent, retention limits tailored to sensitivity, or deletion transparency. In a general-purpose agent, this creates a meaningful privacy and security risk because sensitive user context may be retained and later surfaced beyond the original task need.

Content

No source excerpt is available for this finding.

Ssd 3

High
Category
Not specified by scanner
Confidence
98% confidence
Finding

Automatically injecting long-term memory into all new conversations creates a direct cross-session disclosure channel: prior conversation data, user habits, technical history, or embedded sensitive context may appear in unrelated future interactions. In a general-purpose agent skill, global memory reuse without strict scoping or consent is particularly dangerous because the activation model is broad and the stored content includes durable user-profile information.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
83% confidence
Finding

The primary description is written as a Chinese operational definition and presents the framework behavior in Chinese while also claiming broad applicability to general agents. There is no explicit statement that the user may choose their preferred language for outputs or interaction.

Content

No source excerpt is available for this finding.

Ssd 3

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

Persistent collection and reuse of user preferences, profiles, and habits across sessions creates a cross-session data retention and disclosure risk, especially when stored in general markdown files intended for later retrieval. The danger is amplified because this skill is explicitly designed to mine failures and interactions for reusable long-term knowledge, increasing the chance that personal or sensitive context is retained unnecessarily.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
88% confidence
Finding

The instruction to 'immediately apply' corrective actions without waiting for user confirmation can cause the agent to perform impactful file writes, tool actions, or workflow changes that the user did not approve. In a self-modifying or memory-writing skill, automatic remediation increases the chance of unintended state changes and can compound earlier mistakes.

Content

No source excerpt is available for this finding.

Ssd 3

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The mandatory pre-task reading of recent memory logs can surface prior user data into new tasks without checking whether the information is relevant or appropriate for the current request. This creates a least-privilege failure where unrelated historical context may influence outputs or be exposed in a different conversation flow.

Content

No source excerpt is available for this finding.

Ssd 3

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The L2/L3 schemas encourage storing observations, lessons, decisions, and user preferences in persistent plain-language markdown files, which can accumulate sensitive user data and operational context over time. Because the content is intended for long-term reuse and retrieval, the skill materially increases the chance of privacy leakage, unintended disclosure, or over-collection beyond the immediate task.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The long-term '用户画像/偏好' section documents durable storage of user preferences and profile-like observations without any privacy notice, consent gate, or sensitivity filtering. Persistent profiling can capture behavioral or personal information over time and be reused outside the user's expectations, increasing privacy and misuse risk.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The schema explicitly defines persistent write, replace, and remove operations against daily memory files and a long-term MEMORY.md store, but provides no requirement to notify the user, obtain consent, or distinguish ephemeral from durable storage. In a self-improving agent, this creates a real risk of silent retention or modification of user data and accumulated knowledge, especially because the skill is designed to trigger broadly on memory-related prompts.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

This markdown file presents all instructions and templates in Chinese, which effectively forces a specific language for users. Under the policy, language constraints should either be optional for the user or clearly justified as region-specific; neither is stated here.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
84% confidence
Finding

The protocol text and generated examples are presented entirely in Chinese, which can amount to a language policy violation when no user opt-in or locale justification is provided. The file does not indicate that this skill is intended only for Chinese-speaking users or a China-specific environment.

Content

No source excerpt is available for this finding.

Ssd 3

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

This section directs the agent to mine user interaction history and repeated correction patterns, including user preferences, without defining data minimization, sensitivity filtering, retention limits, or consent boundaries. In a self-improving memory-enabled agent, that creates a persistent profiling path where user-derived behavioral data can be retained and repurposed beyond the immediate task.

Content

No source excerpt is available for this finding.

Ssd 3

Medium
Category
Not specified by scanner
Confidence
97% confidence
Finding

The workflow explicitly records extraction events in persistent memory based on historical operations, creating durable semantic summaries of user activity. Even if raw pattern data is later cleaned from L2, the MEMORY.md record preserves derived history and can accumulate sensitive inferences about projects, habits, or recurring tasks.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The example manifest says the skill should be used when the user asks to generate '数据报告、统计报告、分析报告'. These are broad, everyday request categories without clear constraints, negative examples, or scope limits, which could cause the auto-extracted skill to activate in many unrelated reporting contexts.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Low
Category
Not specified by scanner
Confidence
73% confidence
Finding

The document is entirely in Chinese and does not offer an alternative language or indicate that language selection is user-configurable. This can be a natural-language policy concern if the skill thereby forces a specific language without user opt-in.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.