Security audit

Chatbot Engine

Security checks across malware telemetry and agentic risk

Overview

This is a straightforward chatbot/NLP skill with normal optional LLM-provider and dependency risks, not evidence of hidden or malicious behavior.

Install from a trusted package index and consider pinning dependency versions before production use. Use the mock or local provider for private testing, and only configure OpenAI or Anthropic if you are comfortable sending prompts and recent conversation context to that provider. Avoid saving sensitive chat sessions or knowledge-base content to shared paths.

SkillSpector

Vulnerability Patterns

Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access

Findings (8)

Context-Inappropriate Capability

Medium

Confidence: 86% confidence
Finding: The chatbot dispatches messages to any registered plugin and directly returns plugin.handle(message) without sandboxing, permission checks, or trust boundaries. If an untrusted or unsafe plugin is registered, user input can trigger arbitrary plugin behavior, expanding the chatbot from dialogue handling into unrestricted code-backed actions.

Missing User Warnings

Medium

Confidence: 86% confidence
Finding: The adapter sends prompts and optional conversation context to third-party LLM providers (OpenAI/Anthropic) without any built-in consent, warning, redaction, or policy gate. In a chatbot engine handling multi-turn dialogue, context may contain sensitive user data, so silent transmission to external APIs can cause privacy and compliance issues even if the transport itself is legitimate.

Unpinned Dependencies

Low

Category: Supply Chain
Content: openai>=1.0.0 scikit-learn>=1.3.0 numpy>=1.24.0 pandas>=2.0.0
Confidence: 97% confidence
Finding: The dependency is specified with a lower bound only, which allows future unreviewed versions to be installed. This weakens build reproducibility and can introduce security regressions or supply-chain risk if a later release is vulnerable or incompatible.

Unpinned Dependencies

Low

Category: Supply Chain
Content: openai>=1.0.0 scikit-learn>=1.3.0 numpy>=1.24.0 pandas>=2.0.0 regex>=2023.0.0
Confidence: 98% confidence
Finding: scikit-learn is unpinned, so installations may resolve to different versions over time, including versions with known security issues. Because this package is also flagged separately for advisories, leaving it unpinned increases the chance of pulling an unsafe version and makes remediation harder to verify.

Unpinned Dependencies

Low

Category: Supply Chain
Content: openai>=1.0.0 scikit-learn>=1.3.0 numpy>=1.24.0 pandas>=2.0.0 regex>=2023.0.0
Confidence: 98% confidence
Finding: numpy is declared with a minimum version only, permitting uncontrolled upgrades and non-reproducible builds. Given the package has multiple historical advisories, unpinned resolution increases exposure to vulnerable or unstable versions in the supply chain.

Unpinned Dependencies

Low

Category: Supply Chain
Content: openai>=1.0.0 scikit-learn>=1.3.0 numpy>=1.24.0 pandas>=2.0.0 regex>=2023.0.0
Confidence: 96% confidence
Finding: pandas is unpinned, which allows dependency drift and undermines reproducibility. While exploitation depends on how the library is used, accepting arbitrary future versions can introduce avoidable security and stability issues.

Unpinned Dependencies

Low

Category: Supply Chain
Content: scikit-learn>=1.3.0 numpy>=1.24.0 pandas>=2.0.0 regex>=2023.0.0
Confidence: 95% confidence
Finding: regex is not pinned to an exact version, so builds may change over time without review. This is primarily a supply-chain and reproducibility weakness rather than an immediately exploitable flaw on its own.

Known Vulnerable Dependency: scikit-learn — 6 advisory(ies): CVE-2020-13092 (scikit-learn Deserialization of Untrusted Data); CVE-2024-5206 (scikit-learn sensitive data leakage vulnerability); CVE-2020-28975 (scikit-learn Denial of Service) +3 more

Critical

Category: Supply Chain
Confidence: 82% confidence
Finding: The finding is directionally valid because scikit-learn has had security advisories, but the requirements entry does not pin a specific installed version, so the exact vulnerable state cannot be confirmed from this file alone. In a chatbot engine, scikit-learn may process untrusted model artifacts or user-derived data, which can raise risk if unsafe deserialization or vulnerable components are used elsewhere in the skill.

VirusTotal

65/65 vendors flagged this skill as clean.

View on VirusTotal

Static analysis

No suspicious patterns detected.