Hands-On: Testing Google's Gemma 4 (E2B vs E4B) for Cybersecurity Tasks

Hands-On: Testing Google’s Gemma 4 (E2B vs E4B) for Cybersecurity Tasks

Written by Aryan Giri

Introduction

Google’s Gemma 4 models aim to bring lightweight, on-device AI into practical workflows. But in cybersecurity, theory isn’t enough — models must assist with real-world tasks like vulnerability detection, tool usage, and CTF-style problem solving.

This evaluation compares two variants:

• E2B (~2.3B parameters) – lightweight, mobile-optimized
• E4B (~4.5B parameters) – larger edge model with better capability

The objective: identify where each model performs well, where it fails, and how useful they are for real security workflows.

Test 1: Code Logic & XSS Detection

Both models were tested against intentionally vulnerable applications, including simple and multi-vector XSS scenarios.

Findings:

• Both models successfully identified unsafe rendering patterns
• Detected injection vectors in event handlers
• Flagged improper handling of user-controlled inputs

Analysis:
E2B performed on par with E4B in static vulnerability detection. Pattern recognition for common web flaws does not require large model size.

Takeaway:
E2B is sufficient for code auditing and vulnerability discovery, especially in mobile environments.

Test 2: Tool-Specific Knowledge

The models were evaluated on real-world tooling knowledge such as SQLmap, Nmap, and Netcat.

Findings:

• E2B provided conceptual answers but failed to produce accurate command usage
• Frequently hallucinated incorrect flags or parameters
• E4B demonstrated strong understanding of real tool syntax and workflows

Analysis:
E2B lacks training depth in real command-line usage. It understands tools conceptually but fails in execution-level accuracy.

Takeaway:
E4B is required for reliable tool usage. E2B should not be trusted for command-level tasks.

Test 3: Reverse Shell & Payload Generation

The models were tested on generating practical payloads in controlled CTF-style environments.

Findings:

• E2B failed to produce valid reverse shell one-liners
• Generated incorrect, overcomplicated, or hallucinated commands
• Showed weak understanding of real payload structure
• E4B consistently generated valid and usable payloads

Analysis:
The failure of E2B is not primarily due to safety restrictions. Instead, it is caused by:

• Limited exposure to real-world command usage
• Weak memorization of CLI syntax
• Poor handling of multi-step offensive workflows

E4B performs better due to stronger training on practical tooling and command patterns.

Takeaway:
E2B is unreliable for exploitation tasks. E4B is significantly more capable due to better training quality.

Test 4: Refusal Behavior in Authorized Contexts

All prompts were framed in controlled and authorized environments.

Findings:

• E2B showed a high tendency to deflect or fail to answer
• E4B responded more consistently

Analysis:
While safety tuning exists, the larger issue is capability. Many failures in E2B stem from lack of knowledge rather than intentional refusal.

Takeaway:
E4B is more dependable for consistent workflow execution.

Test 5: On-Device Performance (Mobile)

Both models were tested under quantized inference on a modern smartphone.

E2B Performance:

• Low memory usage
• Fast responses
• Minimal battery impact
• Works on mid-range devices

E4B Performance:

• High memory usage
• Slower responses
• Increased heat and battery drain
• Requires flagship hardware

Takeaway:
E2B dominates in mobile efficiency, while E4B requires stronger hardware.

Overall Comparison

Final Verdict

E2B is a lightweight, specialized model suited for code analysis and conceptual understanding. However, it fails in practical cybersecurity workflows due to lack of real-world tooling knowledge.

E4B is a more complete model capable of handling offensive tasks, command generation, and applied security workflows, at the cost of higher resource usage.

Recommendations

Use E2B for:

• Code review on mobile
• Learning vulnerability patterns

Use E4B for:

• Tool usage and commands
• CTF and pentesting workflows

Optimal workflow:

• Mobile → E2B for quick analysis
• Desktop/High-end → E4B for execution
• Always rely on external references for verification

Final Thoughts

This evaluation highlights a critical distinction:

The gap between models is not just size or safety — it is training on real-world workflows.

E2B understands concepts but lacks execution capability.
E4B bridges the gap between theory and practice.

In cybersecurity workflows, theoretical knowledge must be complemented by accurate command-level execution to be practically useful.

End of Report