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Security audit

scraping-twitter-profiles

Security checks for vulnerabilities and agentic risk

Overview

This skill is purpose-aligned for Apify-based Twitter/X profile scraping, but its documented REST examples handle the Apify token in an unsafe way and its broader graph-collection options deserve review before use.

Review this skill before installing if you will use real Apify credentials or sensitive account lists. Prefer an official Apify client, MCP server, or Authorization header instead of tokenized URLs; use a limited Apify token where possible, avoid sharing logs containing commands, and be clear that submitted handles, URLs, and scraped results are processed by Apify and may include follower/following or retweeter data if those options are enabled.

A.I.G

Vulnerability Patterns
  • 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
  • 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)

T09 · Insecure Skill Coding Practices

Warning
Location
SKILL.md:91
Finding

Apify API Token Exposed Through URL Query Parameters

Content
View full analysis
Remediation
View remediation

SkillSpector

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
91% confidence
Finding

The skill description does not clearly warn that it requires an APIFY_TOKEN and transmits requested Twitter/X account data to the external Apify service. This reduces informed consent and can cause users to submit handles or datasets without understanding third-party processing, credential use, and associated privacy/compliance implications.

Content

No source excerpt is available for this finding.

Description-Behavior Mismatch

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The skill advertises account-level profile export, but its documented inputs also permit collection of follower lists, following lists, and retweeters, which materially expands the scope from simple profile metadata to relationship and engagement graph extraction. This mismatch can mislead users, reviewers, or downstream policy controls into approving broader data collection than intended.

Content

No source excerpt is available for this finding.

External Transmission

Medium
Category
Data Exfiltration
Confidence
86% confidence
Finding

This command sends user-supplied Twitter usernames to a third-party API and includes an authentication token in the request URL, creating external data exposure and increasing the chance of token leakage via shell history, logs, proxies, or monitoring tools. While external transmission is expected for this integration, the documentation does not sufficiently minimize credential exposure or warn about the privacy boundary.

Content

Scanner excerpt · SKILL.md (reported line 91)May include surrounding context.

REST API fallback:

bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"usernames": ["handle1", "handle2"]}'

External Transmission

Medium
Category
Data Exfiltration
Confidence
50% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 92)May include surrounding context.

REST API fallback:

bash
curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"usernames": ["handle1", "handle2"]}'

External Transmission

Medium
Category
Data Exfiltration
Confidence
82% confidence
Finding

The polling example performs another authenticated request with the token embedded in the query string, which repeats the credential exposure problem and reinforces a pattern of unsafe secret handling in documentation. Although polling run status is normal behavior, putting tokens in URLs can leak them through logs and observability systems.

Content

Scanner excerpt · SKILL.md (reported line 99)May include surrounding context.

Save id as RUN_ID. Poll until status = SUCCEEDED:

bash
curl "https://api.apify.com/v2/actor-runs/$RUN_ID?token=$APIFY_TOKEN" | grep '"status"'

Fetch results:

External Transmission

Medium
Category
Data Exfiltration
Confidence
84% confidence
Finding

The dataset fetch example transmits collected results through a third-party API using a token in the URL, exposing both the retrieved data and the credential to logging and history risks. Because output may include profile metadata and potentially broader scraped data depending on inputs, this can increase privacy and compliance exposure.

Content

Scanner excerpt · SKILL.md (reported line 104)May include surrounding context.

Fetch results:

bash
curl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=$APIFY_TOKEN&format=json"

Step 3: Handle Edge Cases

Static analysis

No suspicious patterns detected.