Detecting Malicious Inputs in LLM Pipelines

In production systems, detecting malicious inputs before they reach the LLM is critical.

Many organizations implement input classification models designed to detect suspicious patterns such as:

Prompt injection attempts

Data extraction requests

Jailbreak instructions

Obfuscated commands

Machine learning models such as LightGBM, BERT-based classifiers, or DeBERTa models are commonly used to filter incoming prompts.

These classifiers analyze patterns in the input text and assign risk labels such as:

Benign

Suspicious

Malicious

However, attackers often try to bypass detection using encoding tricks, including:

Base64 encoding

Unicode obfuscation

Random text injection

Gibberish prompts

To address this, modern security pipelines combine multiple detection layers, including:

Heuristic analysis

Machine learning classification

Pattern-based detection

Behavior monitoring

This layered approach significantly improves the robustness of LLM applications.