बीटा Shoulder बीटा में है — परिणाम कभी-कभी गलत हो सकते हैं। आपकी प्रतिक्रिया तय करती है कि हम आगे क्या ठीक करें। प्रतिक्रिया साझा करें
FIX SOON No patch yet
High impact vulnerability.
No patch available yet. Apply workarounds and monitor.

Log4Shell

Remote code execution via JNDI lookup in log messages

Severity CVSS CRITICAL 10
Exploitation Shoulder No known exploits
Fix available Unknown
CVE-2021-44228

Should I care?

This matters if:
  • Apps deserializing data from untrusted sources
  • APIs accepting serialized objects (JSON, XML, YAML)
  • Apps using expression languages (OGNL, SpEL, EL) with user input
  • Template engines that evaluate expressions
  • Apps processing complex user input (files, forms, APIs)
  • Endpoints without schema validation
Not relevant if:
  • You only deserialize data from trusted internal services
  • Input schemas are strictly validated
  • No expression language evaluation in your code
  • All expressions are pre-compiled and static
  • All inputs are validated against schemas
  • You use strict type checking at boundaries

How it breaks apps

1 Untrusted data is deserialized
2 Malicious object is instantiated
3 Gadget chain triggers execution
4 Remote code execution

Is this in your code?

Shoulder scans your codebase and tells you if CVE-2021-44228 is reachable — not just present.

npx @shoulderdev/cli trust .
Technical details

Risk by Environment

Production (public-facing) HIGH
Patch soon. Exploitation requires specific conditions but impact is severe.
Staging MEDIUM
Schedule patch. Review if this environment is network-accessible.
Internal services MEDIUM
Assess exposure. Patch if the service handles sensitive data.
Local dev only LOW
Minimal risk in isolated dev environments.

AI Development Risk

This vulnerability pattern is commonly introduced when AI generates code.

CWE-502
Deserialization of untrusted data
LLMs frequently generate code that deserializes data without validation. AI-scaffolded APIs often trust input formats implicitly.
CWE-20
Improper input validation
LLMs routinely skip input validation, trusting that callers will provide well-formed data.