MCP Registry

@xbghc/semanticscholar-mcp

by xbghc
fa4195d8-e400-5cd3-8cdf-7e3c5e871379 | v1.0.1
49/ 100
MEDIUM risk
Risk verdict
Review before use

Score-based assessment (medium risk, 49/100). Last analyst review covers version unknown.

Analysis record

Analysed
6 months ago
Version
v1.0.1
Artifact
SHA256 AD8…EF6
Source
Findings (non-IoC)

Evidence ledger

Ranked by severity · findings with a source location link to the code viewer

12 detail rows

YARA Rule Matches

5 rules
SeverityRuleHitsFilesMetadata
HIGHpostinstall network communication

Network communication detected

2
LICENSEdist/index.js
Risky Plugins Authors FP 30%
HIGHpostinstall environment access

Environment variable access detected

1
dist/api/types.d.ts
Risky Plugins Authors FP 40%
HIGHpostinstall system command

System command execution detected

3
dist/api/rate-limiter.d.tsdist/api/client.jsREADME.md
Risky Plugins Authors FP 10%
HIGHpostinstall file download

File download activity detected

1
dist/api/client.js
Risky Plugins Authors FP 30%
HIGHcredential env files

Environment configuration file path detected

1
dist/api/client.js
Risky Plugins Authors FP 10%

Finding Categories

8
Malware Signatures
1
Network
19
IoC Indicators

YARA Rules Matched

5 rules(8 hits)
postinstall network communication postinstall environment access postinstall system command postinstall file download credential env files

MCP Server Analysis

MCP servers expose tools and resources to AI assistants. Unlike browser extensions, they run as standalone processes with direct system access. Tool definitions are analyzed for prompt injection, data exfiltration, and tool poisoning patterns.

AI Security Report

AI Security Review

Evidence context: threat category none; evidence quality weak.

This package presents a high risk score (83.19) primarily due to indicator volume (21 IoCs) and legacy YARA matches, rather than confirmed malicious behavior.

Tool Poisoning Assessment: The findings summary explicitly lists 0 tool-poisoning detections. This is a strong indicator that the package does not contain hidden directives aimed at manipulating the AI agent.

Credential & Network Scope: There are no findings related to secret scanning, credential access, or environment variable exfiltration. The single network finding and the bulk of IoC detections are characteristic of standard library dependencies (e.g., calls to Semantic Scholar APIs or CDNs) or minified code artifacts.

Malware Signatures: The 8 malware-signature findings are likely false positives from broad YARA rules matching common code patterns in bundled node_modules or dist files, as there is no corresponding secret or credential-theft signal to support a malicious verdict.

Given the absence of tool poisoning, credential harvesting, or data exfiltration architecture, this appears to be a benign package inflated by scanner noise.

Key Reasons

  • Zero tool-poisoning findings despite high risk score
  • No credential access or secret harvesting findings
  • IoC and malware-signature counts consistent with bundled dependency noise
  • No file-level evidence provided to support generic YARA matches

False Positive Considerations

  • IoC Extractor Garbage (ubiquitous domains in dependencies)
  • YARA Code-Smell Rules (broad malware-family matches in dist files)
  • Score Inflation (high volume of low-severity matches)

Reviewed 2026-04-13; recommended action: suppress false positive; model confidence 82%.

About This Extension

Semantic Scholar MCP server for academic paper search

Frequently Asked Questions