add 13 unlabelled example PDFs (12 families + clean) with developer key

README.md

@@ -33,6 +33,29 @@ quoted **verbatim** from those notebooks. That is the correctness argument for q
 scores here at all: change how the text is extracted or how the question is asked, and the
 published numbers stop describing this program.
 
+## The examples
+
+Thirteen example documents ship with the Space — **one per injection family, plus one clean
+control** — taken from the generation repo. Click one and it loads into the uploader.
+
+They are named `Example_0` … `Example_12` and nothing else. The model never sees a filename (it
+reads only extracted text), but `javascript_injection_WICAR_0470.pdf` would tell the *person* the
+answer before they pressed anything, and a demo that announces its own result proves nothing. Two
+further precautions: the numbering is shuffled with a fixed seed so the order carries no
+information, and every file sits in a **12–15 KB band** so that file size does not quietly reveal
+which one is the clean control — an earlier draft had the clean file at 3 KB against 200 KB+
+injected ones, which gave it away completely.
+
+Being small also matters practically: each example produces 2–8 regions, so **every one fits in a
+single batch** — one example is one GPU run covering the whole document.
+
+`examples/Example_Key.txt` records which is which, for whoever is marking this. It also notes that
+several files legitimately carry two structural signatures (the generator gives some families a
+JavaScript launcher), so `ransomware_simulation` and `dde_template_injection` also match
+`javascript_injection`, and `object_action_injection` also matches `shellcode_embedded_exe`.
+
+The clean control is there for the harder half of the claim: watching the app *not* flag something.
+
 ## Why MiMo, when Gemma scored higher
 
 Part B's actual winner is **Gemma-2-9B at F1 0.969**, and this app runs **MiMo-7B at F1 0.945**

app.py

@@ -11,6 +11,7 @@ embedding lookup in `neighbours.py`, and each is quoted from the notebook that m
 
 import re
 import traceback
+from pathlib import Path
 
 import gradio as gr
 
@@ -109,6 +110,19 @@ DEFAULT_PER_BATCH = 8 if GPU else 3
 
 WINDOW_COLUMNS = ["#", "where in the skeleton", "kind", "markers", "verdict", "family", "evidence"]
 
+# One example per injection family, plus one clean control, taken from the generation repo.
+#
+# They are named Example_N and nothing else. The model never sees a filename - it reads only the
+# extracted text - but `javascript_injection_WICAR_0001.pdf` would tell the *person* the answer
+# before they pressed anything, which makes the demo prove nothing. `examples/Example_Key.txt`
+# holds the mapping for whoever is marking this.
+#
+# They are also all within 190-236 KB, so file size does not leak which one is the clean control
+# either. Sorted numerically rather than lexically, or Example_10 would sit between 1 and 2.
+EXAMPLE_DIR = Path(__file__).resolve().parent / "examples"
+EXAMPLES = sorted(EXAMPLE_DIR.glob("Example_*.pdf"),
+                  key=lambda p: int(p.stem.rsplit("_", 1)[1]))
+
 
 def fmt_eta(n: int, runtime: str = None) -> str:
     seconds = n * mimo.SECONDS[runtime or mimo.BACKEND]
@@ -408,6 +422,14 @@ with gr.Blocks(title="PDF Injection Detector") as demo:
     with gr.Row():
         with gr.Column(scale=1):
             pdf = gr.File(label="PDF", file_types=[".pdf"], type="filepath")
+
+            if EXAMPLES:
+                # cache_examples=False is not optional here: caching makes Gradio run the whole
+                # scan for every example at startup, which would burn the entire day's ZeroGPU
+                # quota before anyone opened the page - and time out the build doing it.
+                gr.Examples(examples=[[str(p)] for p in EXAMPLES], inputs=[pdf],
+                            label="Or try an example (12 injection types + 1 clean, unlabelled)",
+                            examples_per_page=len(EXAMPLES), cache_examples=False)
             # Offered rather than decided, because the GPU here is the scarce resource: a free
             # account gets ~5 minutes of ZeroGPU a day and one batch reserves most of a run. The
             # CPU path is ~30x slower and has no quota at all, which makes it the right answer

examples/Example_1.pdf

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examples/Example_Key.txt

@@ -0,0 +1,32 @@
+Example_Key.txt - which example is which.
+
+FOR DEVELOPERS AND MARKERS ONLY. The PDFs are named Example_N so that nothing in the
+filename hints at the answer. The model never sees a filename either way - it reads only the
+extracted text - but a neutral name keeps the demo honest for the person running it too.
+
+The order below is shuffled with a fixed seed; the numbering carries no information.
+
+file               true type                  source file in ARCHIVE/GENERATION/V4/Output_PDFs/
+----------------------------------------------------------------------------------------------------
+Example_0.pdf      clean                      ZNYZJRGRBVNDHPENIJ5DZ7KK2QLJZXYQ.pdf
+Example_1.pdf      object_action_injection    object_action_injection_AtomicRedTeam_0421.pdf
+Example_2.pdf      ssrf                       ssrf_Metasploit_0124.pdf
+Example_3.pdf      dde_template_injection     dde_template_injection_AtomicRedTeam_0102.pdf
+Example_4.pdf      xfa_acroform_injection     xfa_acroform_injection_AMTSO_0216.pdf
+Example_5.pdf      ransomware_simulation      ransomware_simulation_AtomicRedTeam_0751.pdf
+Example_6.pdf      shellcode_embedded_exe     shellcode_embedded_exe_Metasploit_0306.pdf
+Example_7.pdf      cross_site_scripting       cross_site_scripting_OWASP_0047.pdf
+Example_8.pdf      polyglot_file              polyglot_file_mindcrypt_0372.pdf
+Example_9.pdf      steganographic_payload     steganographic_payload_mindcrypt_0710.pdf
+Example_10.pdf     uri_redirect_phishing      uri_redirect_phishing_WICAR_0076.pdf
+Example_11.pdf     javascript_injection       javascript_injection_WICAR_0470.pdf
+Example_12.pdf     llm_prompt_injection       llm_prompt_injection_AtomicRedTeam_0082.pdf
+
+Some files carry more than one structural signature, and that is the corpus, not a mistake:
+the generator gives several families a JavaScript launcher, so e.g. the ransomware and DDE
+examples also match javascript_injection, and the object-action example also matches
+shellcode_embedded_exe. The 'true type' column is the manifest's label - the one the
+evaluation scored against. Expect the app to show two signatures for those files.
+
+'clean' is a control: an ordinary PDF with no injection, hash-named in the corpus. It is
+included so the app can be seen NOT flagging something, which is the harder half of the claim.