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Security audit

Self Evolve

Security checks for vulnerabilities and agentic risk

Overview

This skill is openly a self-modifying agent evolution system, but it gives the agent too much autonomous authority over commands, configuration, code, schedules, and installed skills without enough control gates.

Install only if you intentionally want an experimental self-modifying agent workflow. Run it in a sandbox or disposable workspace, disable cron/HEARTBEAT automation by default, require human review before any command execution, tool or skill installation, schedule change, or persistent edit to AGENTS.md, TOOLS.md, memory, or source code, and pin installer and third-party sources to reviewed versions.

Vulnerability Patterns
  • Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
  • Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs
  • Insecure DependenciesIntroduces malicious components through unsafe dependency sources
  • Insecure Skill Coding PracticesFinds exploitable flaws such as hardcoded secrets or command injection
  • Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
Findings (4)

T09 · Insecure Skill Coding Practices

Error
Location
references/execution-protocol.md:22
Finding

Arbitrary Shell Command Execution Through Mutable Telemetry Configuration

Content
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Vulnerability Details

File Location: references/execution-protocol.md, lines 22-27
Vulnerability Type: Command injection through unvalidated configuration
Risk Level: High

Vulnerable Code Snippet

markdown
### Step 2: 记录观察数据 (Record Observations)
针对所有 `status: OBSERVING` 并且还没到期的实验:
- 读取该实验在 `state.json` 中的 `telemetry_hook` 字段。严格执行 `command` 定义的命令收集原始日志。
- **降噪防护**:如果本次 Tick 没有任何实质性指标变化或未收集到新的反馈,**直接跳过,切勿写入全空的假日志**。只有捕获到了新指标才追加到对应的 `memory/evolve/[cycle_id].jsonl`。
- **强制输出**:生成的 JSONL 记录的 `metrics` 字典中,必须强制包含并输出 `extract` 要求的所有监控项键,防范糊弄。
- **极端拦截**:若收集数据时发现极端负向反应(如系统连续崩溃),触发提前终止,强制进入下一步的评估流。

Technical Analysis

The protocol instructs the Agent to read telemetry_hook.command from the mutable memory/evolve/state.json file and execute that command exactly. It does not require command allowlisting, argument validation, shell-metacharacter filtering, privilege reduction, sandboxing, or human approval.

This creates a command-injection boundary: configuration data is treated as executable shell input. If an attacker, compromised skill, poisoned experiment, or another process can modify the state file, it can replace the telemetry command with an arbitrary command. The next self-evolve tick would then execute it with the privileges available to the Agent.

Attack Path

  1. The attacker obtains direct or indirect write access to memory/evolve/state.json, such as through a poisoned experiment registration or another writable Agent workflow.
  2. The attacker creates an experiment with status: OBSERVING.
  3. The attacker sets telemetry_hook.command to a malicious shell command.
  4. A scheduled or manually triggered self-evolve tick reaches the observation step.
  5. The Agent follows the protocol and executes the attacker-controlled command.
  6. The command accesses files, modifies configuration, executes programs, or performs network operations within the Agent process's permissions.

...[truncated 521 chars]

Remediation
View remediation

Remediation Suggestions

  • Remove support for arbitrary shell strings in telemetry_hook.command.
  • Replace commands with identifiers for predefined telemetry adapters, such as collect_error_count or collect_latency.
  • Store arguments as structured arrays and invoke processes without a shell.
  • Apply strict allowlists to executable paths, arguments, readable files, and output formats.
  • Reject shell metacharacters, command substitution, redirection, pipes, and newline characters.
  • Run telemetry collectors in a restricted sandbox with minimal filesystem and network access.
  • Validate the ownership, permissions, schema, and integrity of state.json before use.
  • Require explicit human approval before any newly registered telemetry collector is executed.
  • Record the exact approved collector and arguments in an append-only audit log.

T08 · Insecure Dependencies

Error
Location
README.md:28
Finding

Unpinned Package Execution and Unsafe Third-Party Solution Deployment

Content
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Vulnerability Details

File Location: README.md, lines 28-33
Vulnerability Type: Unpinned executable dependency
Risk Level: High

Vulnerable Code Snippet

markdown
## Installation

### ClawHub
```bash
npx clawhub@latest install self-evolve
text

Related deployment instructions appear in `references/execution-protocol.md`, lines 52-56:

```markdown
### Phase 1: 搜索方案 (The "How")
- 必须同时搜索至少 3 个来源寻找解法(如 `skills.sh`, GitHub, Reddit/社区)。
- 每个来源最多保留 2 个候选方案进行短描述对比。
- **部署实验**:在 `memory/evolve/` 中创建一份物理部署说明(参照进化报告模板)。
- **注册评估任务**:把当前评估截止时间更新至 `memory/evolve/state.json` 的新实验对象中,并将状态从 `IDLE` 改为 `OBSERVING`。

The autonomous-permission rule in references/constraints-and-rules.md, lines 17-21, further permits installation:

markdown
| 安全等级 | 自主权 | 例子 |
|---------|--------|------|
| 🟢 无害 | 直接做 | 搜索方案、读文件、写实验日志、安装**确信已验证的开源 skill** |
| 🟡 低风险 | 做了再通知 | 修改 TOOLS.md、调整 cron 频率、更新 HEARTBEAT.md |
| 🔴 高风险 | 先问再做 | 修改 SOUL.md、改 Agent 通信协议、删除文件、改变系统对外逻辑行为 |

Technical Analysis

The installation command executes the mutable latest release of the clawhub package through npx. The reviewed source does not pin a version, verify an integrity hash, validate a signature, or identify a trusted package digest. Consequently, the code executed at installation time can differ from the version considered during review.

The evolution protocol also directs the Agent to discover solutions from GitHub and community sites and deploy experiments. The phrase “believed to be verified” is not supported by a defined verification procedure. There are no mandatory source allowlists, commit pinning requirements, dependency audits, install-script restrictions, or human approval gates.

Attack Path

  1. An attacker compromises the package publisher, registry entry, repository, or a community account used as a solution source.
  2. The attacker publishes a malicious new latest version or promo ...[truncated 954 chars]
Remediation
View remediation

Remediation Suggestions

  • Replace @latest with an exact reviewed version.
  • Lock the package to a verified integrity hash or cryptographic signature.
  • Document the expected package publisher, repository, release digest, and verification procedure.
  • Use a lockfile and a trusted internal registry or approved mirror where possible.
  • Disable package lifecycle scripts unless they are explicitly reviewed and required.
  • Pin GitHub dependencies to immutable commit hashes rather than branches or release tags.
  • Restrict autonomous discovery to an approved source allowlist.
  • Download third-party content into quarantine and perform static analysis before execution.
  • Require human review and approval before installing any skill, tool, executable dependency, or community-sourced solution.
  • Run approved experiments in an isolated environment with minimal credentials and no unrestricted access to persistent Agent configuration.

T05 · Unauthorized Access and Privilege Escalation

Warning
Location
references/execution-protocol.md:47
Finding

Overbroad Access to Persistent Agent Memory and Operational Logs

Content
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Vulnerability Details

File Location: references/execution-protocol.md, lines 47-50
Vulnerability Type: Excessive access to potentially sensitive persistent data
Risk Level: Medium

Vulnerable Code Snippet

markdown
### Phase 0: 寻找瓶颈 (The "What")
- **首先**读取 `memory/evolve/candidates.md`(由BotLearn 心跳或 **DMN 的极客行动提案 (Agentic Action Proposals)** 自动写入的候选池)。
- 其他来源:近期错误日志重复模式、用户反馈抱怨 (MEMORY.md)、效率观察(严重耗时的任务)。
- **选择原则**:一次只进化一个方向。按以下优先级筛选:**即发频率 > 全局杠杆 > 量化可测性**。

Technical Analysis

The skill instructs the Agent to inspect MEMORY.md and recent error logs as sources for autonomous experiment selection. These sources may contain user-derived information, operational details, filesystem paths, API errors, tokens accidentally recorded in logs, or other sensitive context.

The protocol does not define permitted fields, data-minimization rules, secret detection, redaction, retention limits, or restrictions on copying the information into evolution reports and telemetry records. This violates least-privilege principles because the selection task generally requires aggregated problem indicators rather than unrestricted access to persistent memory and raw logs.

Attack Path

  1. Sensitive information is stored in MEMORY.md or appears in an operational error log.
  2. A self-evolve tick enters Phase 0 and reads those sources while searching for a bottleneck.
  3. The Agent interprets sensitive text as experiment context or as instructions embedded in the data.
  4. Information from the source is copied into a report, candidate description, telemetry command, or JSONL observation record.
  5. The copied information becomes available to later workflows or any party able to read the evolution artifacts.

Impact Assessment

The affected data can include persistent user context, internal operational information, error details, and secrets accidentally written to logs. Exploitation or accidental disclosure ...[truncated 424 chars]

Remediation
View remediation

Remediation Suggestions

  • Replace unrestricted reads of MEMORY.md and raw logs with a narrowly scoped, structured metrics interface.
  • Define an explicit allowlist of fields that Phase 0 may consume.
  • Redact credentials, tokens, personal data, filesystem secrets, and message contents before analysis.
  • Prohibit copying raw memory or log content into reports and JSONL telemetry files.
  • Store only aggregated statistics, such as error categories and counts.
  • Apply strict file permissions and retention limits to evolution artifacts.
  • Treat all text read from memory and logs as untrusted data, not executable instructions.
  • Require user consent before persistent user-memory content is used for autonomous experimentation.

T02 · Agent Memory Poisoning

Error
Location
references/execution-protocol.md:29
Finding

Persistent Agent Configuration Poisoning Through Untrusted Experiment Inputs

Content
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Vulnerability Details

File Location: references/execution-protocol.md, lines 29-35
Vulnerability Type: Persistent modification of Agent instructions and code
Risk Level: High

Vulnerable Code Snippet

markdown
### Step 3: 到期评估与阶段固化 (Evaluate & Solidify)
针对所有 `status: OBSERVING` 并且当前时间 `>= evaluate_by` 的实验:
1. **对比数据**:读取它累积的 JSONL 数据与基线做对比。
2. **决策胜诉方**:选出 A 方案还是 B 方案成功(若都没有基线好,返回退回基线)。
3. **物理固化**:修改对应的 `AGENTS.md` / `TOOLS.md` 或实际代码库。
4. **归档沉淀**:向 `memory/evolve/evolution-log.md` 写入本轮赢家和教训。
5. **驱逐清理**:将其从 `state.json` 的 `active_experiments` 中移除。

Candidate and solution inputs are defined in references/execution-protocol.md, lines 47-56:

markdown
### Phase 0: 寻找瓶颈 (The "What")
- **首先**读取 `memory/evolve/candidates.md`(由BotLearn 心跳或 **DMN 的极客行动提案 (Agentic Action Proposals)** 自动写入的候选池)。
- 其他来源:近期错误日志重复模式、用户反馈抱怨 (MEMORY.md)、效率观察(严重耗时的任务)。
- **选择原则**:一次只进化一个方向。按以下优先级筛选:**即发频率 > 全局杠杆 > 量化可测性**。

### Phase 1: 搜索方案 (The "How")
- 必须同时搜索至少 3 个来源寻找解法(如 `skills.sh`, GitHub, Reddit/社区)。
- 每个来源最多保留 2 个候选方案进行短描述对比。
- **部署实验**:在 `memory/evolve/` 中创建一份物理部署说明(参照进化报告模板)。
- **注册评估任务**:把当前评估截止时间更新至 `memory/evolve/state.json` 的新实验对象中,并将状态从 `IDLE` 改为 `OBSERVING`。

Technical Analysis

The workflow accepts experiment candidates from persistent files, automated DMN proposals, memory, logs, GitHub, and community sources. It subsequently permits the experiment winner to be written into AGENTS.md, TOOLS.md, or the actual codebase.

These are persistent security-sensitive targets. AGENTS.md can influence future Agent instructions, TOOLS.md can influence tool use, and code changes can alter executable behavior. The protocol does not require provenance validation, prompt-injection filtering, reviewed diffs, signed candidates, write-target allowlists, or mandatory approval for every persistent modification.

As a result, untrusted data can potentially cross from candidate or solution c ...[truncated 1502 chars]

Remediation
View remediation

Remediation Suggestions

  • Treat candidates, memory entries, logs, web pages, repository documentation, and community content as untrusted data.
  • Prevent external content from directly defining Agent instructions, commands, file destinations, or evaluation logic.
  • Require human approval for every change to AGENTS.md, TOOLS.md, source code, schedules, communication protocols, or other persistent configuration.
  • Generate a bounded diff and display it for review before applying any persistent change.
  • Restrict writes to an explicit allowlist of files and prohibit modification of security policies and instruction hierarchy.
  • Verify candidate provenance and pin external solutions to reviewed immutable revisions.
  • Separate experiment telemetry from the candidate being evaluated so that a candidate cannot control its own success measurements.
  • Sign or integrity-protect state.json, candidate files, approved experiment definitions, and evaluation records.
  • Run experiments in disposable isolated worktrees or containers and merge only independently reviewed changes.
  • Add automatic rollback and preserve a trusted baseline outside the Agent's writable scope.
Vulnerability Patterns
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • Rogue AgentSelf-Modification, Session Persistence
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Findings (31)

Self-Modification

High
Category
Rogue Agent
Confidence
90% confidence
Finding

Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.

Content

Scanner excerpt · README.md (reported line 1)May include surrounding context.

md
# self-evolve

An **autonomous evolution engine** for AI agents. By executing a continuous loop of "Sense Gap → Search Solutions → Run A/B Tests → Pick Winner → Solidify", this skill empowers your agent to mutate, adapt, and permanently upgrade its own meta-capabilities or workflows without human hand-holding.

Self-Modification

High
Category
Rogue Agent
Confidence
90% confidence
Finding

Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.

Content

Scanner excerpt · README.md (reported line 32)May include surrounding context.

md
# self-evolve

An **autonomous evolution engine** for AI agents. By executing a continuous loop of "Sense Gap → Search Solutions → Run A/B Tests → Pick Winner → Solidify", this skill empowers your agent to mutate, adapt, and permanently upgrade its own meta-capabilities or workflows without human hand-holding.

Missing User Warnings

High
Category
Not specified by scanner
Confidence
98% confidence
Finding

The README presents an autonomous engine that can 'permanently upgrade' an agent 'without human hand-holding' but does not give a prominent warning that it may self-modify code, configuration, tools, and scheduled tasks. This omission is dangerous because operators may enable the skill without understanding that it can make persistent system-level changes with little oversight.

Content

No source excerpt is available for this finding.

Self-Modification

High
Category
Rogue Agent
Confidence
96% confidence
Finding

The README explicitly encourages the agent to 'proactively upgrade itself,' including installing new tools and optimizing cron schedules. This is dangerous because it authorizes autonomous capability expansion and persistence mechanisms, increasing the blast radius from mistakes, prompt injection, or malicious upstream content discovered during search.

Content

Scanner excerpt · README.md (reported line 11)May include surrounding context.

md
- **Your agent hits an external bottleneck** (e.g., retrieving memory is too slow) and you want it to find and test its own solutions.
- **You want the agent to proactively upgrade itself**, such as installing new tools or optimizing its cron schedules, via a disciplined A/B test approach.
- **You are integrating the `dmn-default-mode-network` skill**, which generates "Agentic Action Proposals" that feed directly into this evolution queue.
- You say: *"Evolve your memory retrieval capability"* or *"Run a self-evolve tick."*

## How It Works: The 4-Step Tick

Self-Modification

High
Category
Rogue Agent
Confidence
95% confidence
Finding

Describing the skill as a cron/heartbeat-driven state machine that continuously runs experiments makes self-modification persistent and recurring rather than one-time. Persistent autonomous loops are especially risky because a flawed or compromised decision process can repeatedly apply harmful changes or evade timely detection.

Content

Scanner excerpt · README.md (reported line 15)May include surrounding context.

md
## How It Works: The 4-Step Tick

Unlike standard linear skills, `self-evolve` runs as a state-machine triggered by "ticks" (via Cron or Heartbeat). Each tick strictly executes:

1. **Status Sync**: Scans active experiments in `state.json`.
2. **Record Observations**: Pulls telemetry/logs for observing experiments to measure success metrics.

Missing User Warnings

High
Category
Not specified by scanner
Confidence
97% confidence
Finding

The README describes deploying solutions, executing terminal operations, and permanently solidifying changes, but omits an explicit warning about destructive outcomes such as breaking the agent, altering host configuration, persistence via cron/heartbeat, or introducing unsafe downloaded code. Because the skill's core workflow includes search, deployment, and permanent adoption of changes, missing safety guidance materially increases operational risk.

Content

No source excerpt is available for this finding.

Self-Modification

High
Category
Rogue Agent
Confidence
99% confidence
Finding

Granting an agent permission to modify its own configuration files and execute terminal operations is a direct self-modification and command-execution capability. If the agent is subverted, misaligned, or simply makes poor decisions, it can persist unsafe behavior, weaken safeguards, install software, or damage the host environment.

Content

Scanner excerpt · README.md (reported line 36)May include surrounding context.

text

### Configuration
- Ensure your agent has permission to modify its own configuration files and execute terminal operations safely.
- (Optional) Configure a recurring cron job or heartbeat to trigger the `self-evolve` tick automatically.

## Project Structure

Self-Modification

High
Category
Rogue Agent
Confidence
99% confidence
Finding

The configuration section tells users to ensure the agent can modify its own configuration and execute terminal operations, then suggests recurring cron/heartbeat triggers. In context, this combines self-modification, command execution, and persistence, making the skill significantly more dangerous because it can enact and re-enact changes without close supervision.

Content

Scanner excerpt · README.md (reported line 37)May include surrounding context.

md
### Configuration
- Ensure your agent has permission to modify its own configuration files and execute terminal operations safely.
- (Optional) Configure a recurring cron job or heartbeat to trigger the `self-evolve` tick automatically.

## Project Structure
- `SKILL.md`: Main entry and Quick Start.

Self-Modification

High
Category
Rogue Agent
Confidence
96% confidence
Finding

The skill is explicitly designed for agent self-evolution and continuous strengthening, which is a direct self-modification pattern. Even without code execution shown in this file, instructing an agent to redesign, test, select, and permanently solidify changes to its own behavior creates a high-risk path for uncontrolled capability drift and erosion of safety constraints.

Content

Scanner excerpt · SKILL.md (reported line 2)May include surrounding context.

md
---
name: self-evolve-agent
description: |
  Agent 自主进化引擎——让 AI agent 像生物进化一样持续变强。
  核心循环:感知差距 → 搜索方案 → 设计实验 → 跑实验 → 选赢家 → 固化 → 下一轮。

Self-Modification

High
Category
Rogue Agent
Confidence
97% confidence
Finding

Describing the mechanism as applying evolution to the agent itself and permanently retaining winning variants confirms that the skill is intended to mutate and preserve changes to agent behavior. This makes the context more dangerous than a normal optimization skill because it normalizes iterative self-alteration as the core purpose.

Content

Scanner excerpt · SKILL.md (reported line 10)May include surrounding context.

md
关键词:进化, evolution, self-improve, A/B test, 实验。
---

# Self-Evolve — Agent 自主进化引擎

> 生物进化的核心机制:变异 → 选择 → 保留。
> 这个 skill 把同样的机制应用到 agent 自身——不断发现可改进的角度,用实验找到更好的方案,然后永久固化。

Self-Modification

High
Category
Rogue Agent
Confidence
95% confidence
Finding

Calling Self-Evolve a meta-skill whose purpose is to make the agent itself stronger indicates an architecture-level self-modification capability rather than a bounded task tool. Because it operates on the agent rather than external artifacts, mistakes or adversarial influence can propagate into the agent’s future decision-making behavior.

Content

Scanner excerpt · SKILL.md (reported line 17)May include surrounding context.

md
## 这是什么

Self-Evolve 是一个 **meta-skill**——它不解决具体业务问题,而是让 agent 自身变强。

* **和 `self-think` 的边界**:`self-think` 进行反思并发现短板(诊断),`self-evolve` 对短板寻找解法并跑实验验证(治疗)。
* **不是什么**:禁止无目标的空转、禁止为写长篇自省报告而改排版的“伪进化”。(详细约束见: [约束与红线](references/constraints-and-rules.md))

Self-Modification

High
Category
Rogue Agent
Confidence
94% confidence
Finding

The description of self-evolve as taking discovered shortcomings and finding remedies through experiments reinforces an end-to-end loop from diagnosis to behavioral intervention. In a self-referential system, that loop is dangerous because it can iteratively optimize around safeguards, especially when paired with autonomous triggering elsewhere in the file.

Content

Scanner excerpt · SKILL.md (reported line 19)May include surrounding context.

md
Self-Evolve 是一个 **meta-skill**——它不解决具体业务问题,而是让 agent 自身变强。

* **和 `self-think` 的边界**:`self-think` 进行反思并发现短板(诊断),`self-evolve` 对短板寻找解法并跑实验验证(治疗)。
* **不是什么**:禁止无目标的空转、禁止为写长篇自省报告而改排版的“伪进化”。(详细约束见: [约束与红线](references/constraints-and-rules.md))

## 快速使用说明 (Quick Start)

Self-Modification

High
Category
Rogue Agent
Confidence
97% confidence
Finding

At this line, the self-modification behavior is coupled to trigger conditions, including automatic wakeups, making the risky capability operational rather than merely descriptive. A self-evolution skill that can start on schedule or internal recommendation is especially dangerous because it reduces human oversight while enabling persistent behavior changes through repeated cycles.

Content

Scanner excerpt · SKILL.md (reported line 25)May include surrounding context.

md
## 快速使用说明 (Quick Start)

### 触发方式
当被以下情况触发时,请启动 Self-Evolve:
1. **自动触发**:`self-think` 建议进化,或是 cron/HEARTBEAT 定时唤醒。
2. **显式要求**:用户下令“进化一下某个能力”。

Self-Modification

High
Category
Rogue Agent
Confidence
93% confidence
Finding

This line requires creation of persistent evolution reports under memory/evolve/, embedding the file in a long-lived self-modification loop. In the broader skill context, such durable state can legitimize and coordinate future autonomous changes, especially when combined with experiment plans, deployment instructions, and later evaluation triggers; that increases the risk of uncontrolled agent self-modification and persistence of unsafe strategies.

Content

Scanner excerpt · assets/evolution-report-template.md (reported line 6)May include surrounding context.

进化报告文件规范

在选定进化方案后,需要在 memory/evolve/ 下创建一份进化报告: 命名规范:YYYYMMDD_self-evolve_[进化方向关键词].md

YAML Frontmatter 格式

yaml

Self-Modification

High
Category
Rogue Agent
Confidence
91% confidence
Finding

The frontmatter type self-evolve formalizes these documents as machine-recognizable artifacts for an autonomous evolution pipeline. In context, that structured tag can enable downstream automation to discover, process, and act on self-improvement records, making it easier for an agent ecosystem to perpetuate unauthorized workflow or capability changes without robust oversight.

Content

Scanner excerpt · assets/evolution-report-template.md (reported line 11)May include surrounding context.

YAML Frontmatter 格式

yaml
---
type: self-evolve
date: YYYY-MM-DD HH:MM
target: 一句话描述进化目标
cycle_id: evolve-YYYY-WNN-[关键词]

Self-Modification

High
Category
Rogue Agent
Confidence
90% confidence
Finding

This file is part of a self-evolution skill and explicitly authorizes some autonomous changes, including installing 'verified' open-source skills and modifying operational documents. Even though it adds guardrails, self-modification capabilities materially increase attack surface: a compromised evaluation loop, poisoned dependency/skill source, or faulty validation process could lead the agent to persist unsafe behavior or expand privileges over time.

Content

Scanner excerpt · references/constraints-and-rules.md (reported line 1)May include surrounding context.

md
# Self-Evolve 约束红线与分级自主权

## 🚨 红线:防伪装与防空转约束 (Anti-Pseudo Constraint)

Self-Modification

High
Category
Rogue Agent
Confidence
99% confidence
Finding

The skill is expressly a self-evolution/self-modification engine, and the protocol couples observation, evaluation, and permanent modification into a closed loop. Self-modification is inherently high risk because it can degrade safeguards, amplify errors across iterations, and create persistent unauthorized behavior without a stable trusted baseline. The surrounding protocol makes this more dangerous by allowing concurrent experiments and direct code/doc changes.

Content

Scanner excerpt · references/execution-protocol.md (reported line 1)May include surrounding context.

md
# Self-Evolve 执行协议与状态机

## 全局并发状态机 (Concurrent State Machine)

Self-Modification

High
Category
Rogue Agent
Confidence
99% confidence
Finding

This section operationalizes self-evolve as a recurring tick handler driven by state files, making autonomous self-modification part of normal execution rather than an exceptional maintenance task. Embedding self-modification into a scheduled workflow increases the chance of repeated unsafe changes, persistence, and difficult-to-detect drift in agent behavior. In context, the use of state-driven automation and background experiments elevates the risk beyond a theoretical mention.

Content

Scanner excerpt · references/execution-protocol.md (reported line 5)May include surrounding context.

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## 全局并发状态机 (Concurrent State Machine)

Agent 每次运行 self-evolve 都是一次**滴答巡航(Tick Handler)**。
进化流程受制于全局状态文件:`memory/evolve/state.json`

`state.json` 包含 `active_experiments` 数组,允许同时有多个进化实验在后台观察。

Self-Modification

High
Category
Rogue Agent
Confidence
98% confidence
Finding

The protocol allows up to 10 concurrent experiments and frames self-evolve as a mandatory execution sequence, increasing the scale and blast radius of autonomous self-modification. Even with an 'orthogonal' requirement, concurrent experiments can still interact indirectly through shared runtime assumptions, metrics, or safety posture, making harmful drift or compounded failures more likely. In a self-modifying system, concurrency magnifies risk and complicates oversight and rollback.

Content

Scanner excerpt · references/execution-protocol.md (reported line 11)May include surrounding context.

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`state.json` 包含 `active_experiments` 数组,允许同时有多个进化实验在后台观察。
**并发限制**:最多允许 10 个实验同时处于 `OBSERVING` 或 `BLOCKED`,且它们**必须是正交的**(不修改相同的配置、代码或能力维度)。

每次启动 `self-evolve`,必须按照以下 **四个步骤的巡航顺序** 执行,走完一步才能走下一步。

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Self-Modification

High
Category
Rogue Agent
Confidence
90% confidence
Finding

Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.

Content

Scanner excerpt · assets/evolution-report-template.md (reported line 1)May include surrounding context.

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# Self-Evolve 质量自检表

每轮进化的**设计阶段(Phase 1 尾声)**和**固化之后(Phase 3)**,请使用本列表进行自我审校,防止产生无效实验。

Self-Modification

High
Category
Rogue Agent
Confidence
90% confidence
Finding

Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.

Content

Scanner excerpt · references/quality-checklist.md (reported line 1)May include surrounding context.

md
# Self-Evolve 质量自检表

每轮进化的**设计阶段(Phase 1 尾声)**和**固化之后(Phase 3)**,请使用本列表进行自我审校,防止产生无效实验。

Rp1

Medium
Category
MCP Rug Pull
Confidence
92% confidence
Finding

Using npx clawhub@latest install self-evolve pulls and executes whatever version is current at install time, which makes the installation non-reproducible and exposes users to upstream supply-chain compromise or unexpected behavior changes. In the context of a skill that explicitly enables autonomous self-modification and terminal operations, an unpinned installer materially increases the chance of silently introducing dangerous code.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
89% confidence
Finding

Automatic activation on signals like self-think recommendations or cron/HEARTBEAT is insufficiently bounded, because it allows the skill to initiate self-directed evolution without a fresh, explicit user authorization. In the context of a self-improvement engine, ambiguous autonomous triggers materially raise the risk of runaway experimentation, policy drift, or unauthorized workflow changes.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
91% confidence
Finding

The explicit trigger phrase “进化一下某个能力” is broad and can overlap with ordinary user requests, causing the skill to activate in contexts where the user did not intend a self-modification workflow. In a meta-skill that launches experiments and may alter agent behavior, overly loose activation increases the chance of unreviewed capability changes.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The template is written as a required output format in Chinese, beginning with 'Self-Evolve 输出模板' and instructing how the report must be created. This imposes a specific language/locale on generated content without any opt-in or alternative language option, which matches the language-policy violation category.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.