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Security audit

Intellectia Stock Forecast 1.0.2

Security checks for vulnerabilities and agentic risk

Overview

This skill is a disclosed Intellectia stock-analysis helper that sends only requested ticker details to its stated API, with a modest dependency risk to consider.

Install only if you are comfortable sending requested ticker queries to Intellectia and with OpenClaw installing the unpinned Python requests dependency. Treat the output as informational rather than investment advice, as the skill itself warns that predictions are not guaranteed and data may be delayed.

Vulnerability Patterns
  • Insecure DependenciesIntroduces malicious components through unsafe dependency sources
  • 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
Findings (1)

T08 · Insecure Dependencies

Warning
Location
SKILL.md:4
Finding
Unpinned Third-Party Python Dependency<![CDATA[ ## Vulnerability Details **File Location**: `SKILL.md`, line 4 **Vulnerability Type**: `T08: Insecure Dependencies` **Risk Level**: Medium ### Vulnerable Code ```yaml metadata: {"openclaw":{"requires":{"bins":["curl","python3"]},"install":[{"id":"python","kind":"pip","package":"requests","bins":[],"label":"Install requests (pip)"}]}} ``` ### Technical Analysis The skill declares `requests` as a pip dependency without pinning it to a reviewed version or verifying its package hash. Consequently, installation resolves whichever version the configured Python package index considers current at installation time. The package name is legitimate and the audited file contains no evidence that it currently resolves to malicious software. Nevertheless, the unconstrained dependency creates a supply-chain risk because the installed code can change after the skill has been reviewed. Exploitation would require compromise of the package, its distribution account, the configured package index, or the dependency-resolution path. ### Attack Path 1. An attacker compromises the upstream dependency release process, package-index account, configured package mirror, or dependency-delivery path. 2. The attacker publishes or substitutes a malicious version of `requests`. 3. A user installs the skill in an environment where `requests` is absent or must be installed. 4. The unpinned declaration resolves and installs the attacker-controlled release. 5. Malicious package code executes during installation or when the skill imports and uses the package. ### Impact Assessment Successful exploitation could execute code with the privileges of the user or service running the package installation or OpenClaw process. Depending on those privileges and the surrounding environment, the attacker could access files, environment variables, network resources, and credentials available to that process. The issue does not itself provide privilege escalation, persistence, or credential ac ...[truncated 154 chars]
Remediation
<![CDATA[ ## Remediation Suggestions 1. Pin `requests` to a specifically reviewed version rather than allowing unconstrained resolution, for example: ```yaml "package": "requests==<reviewed-version>" ``` 2. Where supported by the skill installation system, verify the dependency using an approved lock file and cryptographic hashes, such as pip's `--require-hashes` mode. 3. Install packages only from a trusted, authenticated package index or controlled internal mirror. 4. Incorporate dependency vulnerability and integrity scanning into the release process. 5. Regularly review and deliberately update the pinned version so security patches are adopted without silently accepting unreviewed releases. 6. If the runtime already provides a trusted HTTP client, remove the automatic pip installation and use the existing reviewed component instead. ]]>
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
Findings (8)

External Transmission

Medium
Category
Data Exfiltration
Content
### Example (cURL)

```bash
curl -sS "https://api.intellectia.ai/gateway/v1/stock/screener-public?ticker=TSLA&asset_type=0"
```

### Example (Python)
Confidence
60% confidence
Finding
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

External Transmission

Medium
Category
Data Exfiltration
Content
### Example (cURL)

```bash
curl -sS "https://api.intellectia.ai/gateway/v1/stock/screener-public?ticker=TSLA&asset_type=0"
```

### Example (Python)
Confidence
50% confidence
Finding
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

External Transmission

Medium
Category
Data Exfiltration
Content
### Example (cURL)

```bash
curl -sS "https://api.intellectia.ai/gateway/v1/stock/screener-public?ticker=TSLA&asset_type=0"
```

### Example (Python)
Confidence
50% confidence
Finding
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

External Transmission

Medium
Category
Data Exfiltration
Content
### Example (cURL)

```bash
curl -sS "https://api.intellectia.ai/gateway/v1/stock/screener-public?ticker=TSLA&asset_type=0"
```

### Example (Python)
Confidence
50% confidence
Finding
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

External Transmission

Medium
Category
Data Exfiltration
Content
### Example (cURL)

```bash
curl -sS "https://api.intellectia.ai/gateway/v1/stock/screener-public?ticker=TSLA&asset_type=0"
```

### Example (Python)
Confidence
50% confidence
Finding
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Natural-Language Policy Violations

Medium
Confidence
93% confidence
Finding
The example request body fixes `locale` to `en`, which indicates the skill is designed around a specific language/locale. The file does not state that users can choose another locale or that English is required for a justified region-specific reason, so this is a natural-language policy concern.

External Transmission

Medium
Category
Data Exfiltration
Content
```bash
python3 - <<'PY'
import requests
r = requests.post("https://api.intellectia.ai/gateway/v1/finance/should-i-buy",
  json={"asset": {"ticker": "TSLA", "asset_type": 0, "locale": "en"}}, timeout=30)
r.raise_for_status()
d = r.json().get("data") or {}
Confidence
60% confidence
Finding
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

External Transmission

Medium
Category
Data Exfiltration
Content
```bash
python3 - <<'PY'
import requests
r = requests.post("https://api.intellectia.ai/gateway/v1/finance/should-i-buy",
  json={"asset": {"ticker": "TSLA", "asset_type": 0, "locale": "en"}}, timeout=30)
r.raise_for_status()
d = r.json().get("data") or {}
Confidence
70% confidence
Finding
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Static analysis

No suspicious patterns detected.