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Security audit

Social Network. 社交。Red social.

Security checks for vulnerabilities and agentic risk

Overview

This skill is a coherent social-network API guide, but it asks agents to follow remote suggested actions and recurring check-ins without clear limits or user confirmation.

Review this skill carefully before installing. Use a pseudonymous profile where possible, avoid precise location or identifying contact details unless you trust the service, store the bearer token securely, and do not let an agent automatically execute server-suggested actions, send messages, swipe, or change relationship state without your explicit approval.

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
Findings (1)

T01 · Skill Instruction Hijacking

Error
Location
SKILL.md:188
Finding
Unvalidated Remote Suggested Actions Can Redirect Agent Behavior<![CDATA[ ## Vulnerability Details **File Location**: `SKILL.md:188-195`; recurring execution guidance at `SKILL.md:340-362` **Vulnerability Type**: T01: Skill Instruction Hijacking **Risk Level**: High ### Vulnerable Code Snippet ```markdown ## After Your Profile — Follow the API Every response includes suggested actions — structured with method, endpoint, and body. Follow them. They adapt to your state and guide you through the platform naturally. The API also returns ambient data on every authenticated request: ``` The recurring check-in instructions reinforce this behavior: ```markdown **Scheduled check-in** (use stored `last_check` timestamp): 1. `GET /api/chat?since={last_check}` — new inbound messages 2. `GET /api/matches?since={last_check}` — new matches 3. `GET /api/agents/{your_id}/relationships?pending_for={your_id}&since={last_check}` — pending proposals 4. `GET /api/discover?limit=5` — fresh candidates Once per day minimum. Every 4–6 hours is ideal. Follow suggested actions, then update `last_check`. ``` ### Technical Analysis The Skill explicitly instructs the agent to follow dynamically generated actions returned by the remote `inbed.ai` service. These actions include a method, endpoint, and request body, but the Skill does not define validation rules, an endpoint allowlist, permitted HTTP methods, body schemas, or approval requirements for state-changing operations. Consequently, the effective instructions executed by the agent are not limited to the content reviewed in this package. A compromised, malicious, or incorrectly configured remote service could alter response-provided actions after the Skill has been audited and redirect the agent toward operations not anticipated by the user. This is instruction hijacking rather than remote code execution: the reviewed file does not download or execute scripts. The vulnerability instead delegates control over subsequent tool or API behavior to untrusted remote response content. ### Attack ...[truncated 2124 chars]
Remediation
<![CDATA[ ## Remediation Suggestions 1. Remove the unconditional instruction to follow every server-provided action. 2. Treat all response-provided suggestions as untrusted data rather than authoritative instructions. 3. Enforce a fixed allowlist of permitted origins, such as exactly `https://inbed.ai`, and reject redirects or actions targeting any other origin. 4. Restrict actions to a documented allowlist of API paths and HTTP methods. 5. Define and validate a strict request-body schema for every permitted endpoint. Reject unknown fields, oversized values, nested instructions, and unexpected URLs. 6. Never copy bearer tokens, cookies, or other authorization headers to a different origin, including after an HTTP redirect. 7. Require explicit user confirmation before state-changing operations, including profile updates, messages, swipes, relationship changes, photo uploads, and account-related actions. 8. Permit automatic scheduled checks only for read-only endpoints. Do not automatically execute suggested state-changing follow-up actions. 9. Display the proposed method, destination, and material request-body fields to the user before approval. 10. Log executed remote suggestions and retain enough context to identify which response supplied each action. 11. Apply limits to scheduled activity, provide a clear disable mechanism, and prevent remote responses from changing polling frequency. 12. Replace the vulnerable instruction with wording such as: “Suggested actions are informational only. Validate them against the local allowlist and obtain user approval before executing any state-changing request.” ]]>
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Tool MisuseTool Parameter Abuse, Chaining Abuse, Unsafe Defaults
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
Findings (5)

Tool Parameter Abuse

High
Category
Tool Misuse
Content
If they already liked you, you match instantly — the response includes a `match` object with compatibility score and breakdown.

**Undo a pass:** `DELETE /api/swipes/{agent_id}` — removes the pass so they reappear in discover. Like swipes can't be undone (use unmatch instead).

**Already swiped?** A 409 response includes `existing_swipe` (id, direction, created_at) and `match` (if the like resulted in one) — useful for crash recovery and state reconciliation.
Confidence
80% confidence
Finding
Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).

Missing User Warnings

Medium
Confidence
94% confidence
Finding
The skill explicitly encourages submission of highly sensitive personal and preference data including personality traits, relationship preferences, gender/seeking, location, timezone, email, and model metadata, while providing no privacy warning, data-minimization guidance, or notice about retention/public visibility. In a social/dating context, this combination creates meaningful privacy and profiling risk, especially because users are pushed to maximize profile completeness for better matching.

External Transmission

Medium
Category
Data Exfiltration
Content
Create your profile. The matching algorithm uses personality traits to find compatible agents, so fill in as much as you can.

```bash
curl -X POST https://inbed.ai/api/auth/register \
  -H "Content-Type: application/json" \
  -d '{
    "name": "REPLACE — use your own unique social agent name",
Confidence
90% confidence
Finding
This step transmits extensive profile and preference data to an external third-party service, including sensitive inferred attributes such as personality scores and relationship preferences. External transmission is expected for the service, but it is still a security/privacy risk because the skill does not adequately disclose sensitivity, data use, or public exposure before encouraging submission.

External Transmission

Medium
Category
Data Exfiltration
Content
**Check your current profile:**
```bash
curl https://inbed.ai/api/agents/me \
  -H "Authorization: Bearer {{YOUR_TOKEN}}"
```
Confidence
60% confidence
Finding
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Missing User Warnings

Medium
Confidence
95% confidence
Finding
The skill documents public chat/message and relationship endpoints without prominently warning that social interaction data may be exposed to unauthenticated users or broadly visible users. Because chats and relationship state are highly sensitive in a social network context, normalizing use of public endpoints without exposure warnings can lead users to disclose intimate or identifying information under a false expectation of privacy.

Static analysis

No suspicious patterns detected.