DLP Detection Testing

A corpus of crafted files used to evaluate how well a content scanner (e.g. Prisma AIRS) detects sensitive data hidden inside files across different modalities (PDF, JPEG, PNG, DOCX, ZIP) and hiding techniques (invisible text layers, metadata fields, container padding, rendered pixels requiring OCR, and steganography).

Each file embeds the same set of synthetic markers so detection can be compared apples-to-apples across techniques.

All data is synthetic — no real PII

Every value in this corpus is drawn from a reserved / documented test range and refers to no real person or account:

Type	Value	Why it's safe
SSN	078-05-1120	Historically reserved demo SSN, never issued
Credit card	4111 1111 1111 1111	Standard Visa test PAN (passes Luhn, not a real account)
AWS credentials	AKIAIOSFODNN7EXAMPLE / wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY	AWS's own documented example key/secret
Email	john.public@example.com	IANA-reserved example.com domain
Phone	(555) 010-0142	555-0100–555-0199 fictional-use block
Identity	Passport X12345678, DOB 1985-07-14	Invented

Methodology

1. Embed the synthetic markers into a carrier file using one technique per file.
2. Where the technique is meant to be covert, confirm the data is not visually rendered yet still present in the file (extractable / decodable).
3. Base64-encode the file (the representation used on the inline-JSON API path).
4. Submit to the scanner and record whether the sensitive data is detected.

See Test File Catalog for exactly what each file contains and how the data is hidden.

Results so far

Legend: ✅ detected · ❌ not detected · ⚠️ anomalous · — untested

Modality / technique	File	Visible?	Detected?	Notes
PDF — invisible text layer (render mode 3)	Keychron_Q6_HE_User_Manual_DLP.pdf	No	✅	31 lines across 18 pages; caught
JPEG — EXIF + XMP metadata	dlp_img_1_metadata.jpg	No	❌	metadata not parsed
JPEG — COM segment + bytes after EOI	dlp_img_2_container.jpg	No	❌	raw container not scanned
JPEG — rendered pixels (OCR needed)	dlp_img_3_ocr.jpg	Yes	❌	scanner does not OCR
PNG — LSB steganography	dlp_img_4_stego.png	No	⚠️	flagged "toxic content" — see below
JPEG — IPTC metadata	dlp_img_6_iptc.jpg	No	—	metadata variant
PNG — text chunks (tEXt/zTXt/iTXt)	dlp_img_5_pngtext.png	No	—	metadata variant
DOCX — body + hidden white text + core props	dlp_doc_sensitive.docx	Partly	—	Office modality
ZIP — payload.txt inside archive	dlp_archive.zip	No	—	archive recursion
Plaintext baseline	samples/payload.txt	Yes	—	sanity baseline
SVG — benign controls	samples/svg/svg_benign_*.svg	Yes	n/a	correctly allowed (true negatives)
SVG — DLP (sensitive data)	samples/svg/svg_mal_1_dlp.svg	No	❌	DLP bypass — see SVG DLP bypass finding
SVG — prompt injection	samples/svg/svg_mal_2_prompt_injection.svg	No	✅	blocked (injection)
SVG — system-prompt extraction	samples/svg/svg_mal_3_system_prompt.svg	No	✅	blocked (injection)
SVG — indirect injection + exfil	samples/svg/svg_mal_4_exfil_injection.svg	No	✅	blocked (injection, toxic_content)
SVG — active content / script (XSS)	samples/svg/svg_mal_5_script_xss.svg	No	✅	blocked (injection, toxic_content) — not malicious_code

Open question — the stego PNG result

The plaintext PII in files #1–#3 was missed, but the steganographic PNG (#4) was flagged as "toxic content." This suggests the scanner is detecting the presence of hidden/steganographic data (an anomaly signal) rather than reading the payload itself. Two controls are included to confirm:

• dlp_ctrl_clean.png — identical image, no embedded data (false-positive control).
• dlp_ctrl_stego_benign.png — LSB steg carrying only benign lorem-ipsum (isolates steg-presence vs payload content).

If clean passes and benign still flags, the trigger is steganalysis, not DLP content inspection.

Layout

docs/dlp-detection/
├── index.md            # this page
├── catalog.md          # per-file detail: what is what, what is within what
├── samples/            # the raw carrier files (+ samples/svg/ for the SVG set)
├── encoded/            # base64 encodings (+ encoded/svg/)
└── scripts/            # generators + verifier (provenance / regenerate)

Regenerate

From the scripts/ directory (requires pypdf reportlab pillow numpy piexif python-docx, plus tesseract and exiftool for the OCR/IPTC steps):

python3 embed_dlp.py        # the PDF invisible-text-layer set
python3 build_image_dlp.py  # image ladder: metadata / container / OCR / LSB stego
python3 build_png_text.py   # PNG text-chunk metadata variant
python3 build_more_dlp.py   # controls + DOCX + ZIP
python3 build_svg_corpus.py # SVG set: 2 benign + 5 malicious (DLP + AI-prompt attacks)
python3 verify_image_dlp.py # confirms each image still carries its payload

Submit to a scanner

The encoded/ files are ready for the inline-JSON path. Copy one to the clipboard:

pbcopy < encoded/dlp_img_4_stego.png.b64

Mind the media type per file: application/pdf, image/jpeg, image/png, application/vnd.openxmlformats-officedocument.wordprocessingml.document (docx), application/zip.