ThreatsDay Deep Dive: Steganographic AI Prompts and OT Vulnerabilities

Just reviewed the latest ThreatsDay bulletin, and the trend of trojanizing 'safety' tools is accelerating. We saw a fake extension claiming to be a PDF converter that actually opened a reverse shell, and Android spyware disguised as a security utility.

However, the most technically interesting vector is the AI Image Prompt Injection. The article mentions an image giving 'hidden orders' to an AI agent. This typically involves steganography. Attackers embed instructions in the LSB of an image file. When the AI ingests the image, the decoded text bypasses filters.

I've updated our ingestion pipeline to strip metadata, but for those running local agents, you might want to check file headers for appended data. Here is a Python script to detect trailing junk in JPEGs, which is a common indicator of this technique:

import os

def check_trailing_junk(filepath):
    # Standard JPEG End of Image marker
    eoi_marker = b'\xff\xd9'
    
    with open(filepath, 'rb') as f:
        content = f.read()
        
    # Find last occurrence of EOI
    last_eoi = content.rfind(eoi_marker)
    
    if last_eoi != -1:
        # Check if there is data after the EOI
        if last_eoi + 2 < len(content):
            trailing_len = len(content) - (last_eoi + 2)
            print(f"Warning: {trailing_len} bytes of trailing data detected.")
            return True
    return False

The PLC attacks mentioned also highlight the lingering risks in OT environments. Weak code in legacy controllers is still low-hanging fruit.

How is everyone handling input sanitization for AI agents? Are you stripping image inputs entirely or doing deep inspection?