diff --git a/nextcloud_mcp_server/document_processors/classifier.py b/nextcloud_mcp_server/document_processors/classifier.py
index 7ce28fd4..7647769f 100644
--- a/nextcloud_mcp_server/document_processors/classifier.py
+++ b/nextcloud_mcp_server/document_processors/classifier.py
@@ -272,6 +272,10 @@ def classify_pdf(content: bytes) -> DocClassification:
     ocr_frac = (sum(p.needs_ocr for p in pages) / sampled) if sampled else 0.0
     # Char-weighted doc-level control-char ratio (p.control_ratio * char_count is
     # the per-page bad-char count). The glyph-leak signal -- see _control_char_ratio.
+    # NOTE: this is over the <=MAX_SAMPLED_PAGES sample, so unlike classify_from_text
+    # (which scans the whole full_text) this diagnostic path can under-detect
+    # corruption concentrated outside the sampled pages. Acceptable here: the hot
+    # path is classify_from_text; this standalone pass is for diagnostics.
     control_ratio = (
         sum(p.control_ratio * p.char_count for p in pages) / total_chars
         if total_chars
diff --git a/nextcloud_mcp_server/observability/metrics.py b/nextcloud_mcp_server/observability/metrics.py
index f2ca1060..26e20421 100644
--- a/nextcloud_mcp_server/observability/metrics.py
+++ b/nextcloud_mcp_server/observability/metrics.py
@@ -334,7 +334,7 @@ document_classifier_flag_total = Counter(
     # so flag{image_heavy} is expected to exceed classified{recommended_tier=ocr}.
     "astrolabe_document_classifier_flag_total",
     "Tier-0 classifier flags raised on documents",
-    ["flag"],  # image_heavy | scanned | bad_text_layer
+    ["flag"],  # image_heavy | scanned | bad_text_layer | corrupt_glyphs
 )
 
 document_text_quality = Histogram(
diff --git a/tests/unit/test_doc_classifier.py b/tests/unit/test_doc_classifier.py
index 2a7d9ee3..e9dc9423 100644
--- a/tests/unit/test_doc_classifier.py
+++ b/tests/unit/test_doc_classifier.py
@@ -31,6 +31,18 @@ def _digital_pdf(
     return data
 
 
+def _glyph_corrupt_pdf(pages: int = 2) -> bytes:
+    # A born-digital PDF whose text layer carries the glyph-leak control chars,
+    # for the classify_pdf (diagnostic) path. pymupdf round-trips the C0 controls.
+    doc = pymupdf.open()
+    for _ in range(pages):
+        page = doc.new_page(width=595, height=842)
+        page.insert_text((50, 60), GLYPH_CORRUPT_TEXT)
+    data: bytes = doc.tobytes()
+    doc.close()
+    return data
+
+
 def _full_page_image_pdf(pages: int = 2) -> bytes:
     # A page whose entire area is a raster image -> looks scanned.
     doc = pymupdf.open()
@@ -381,3 +393,13 @@ def test_empty_doc_routes_ocr_not_structured():
     c = clf.classify_from_text("", [{"page": 1, "start_offset": 0, "end_offset": 0}])
     assert c.recommended_tier == "ocr"
     assert "corrupt_glyphs" not in c.flags
+
+
+def test_classify_pdf_glyph_corrupt_routes_structured():
+    # Symmetry with the classify_from_text routing on the standalone/diagnostic
+    # classify_pdf path (which re-opens the PDF and samples pages).
+    c = clf.classify_pdf(_glyph_corrupt_pdf())
+    assert c.recommended_tier == "structured"
+    assert "corrupt_glyphs" in c.flags
+    assert c.mean_control_ratio > clf.GLYPH_CORRUPTION_RATIO
+    assert c.mean_text_quality >= clf.MIN_TEXT_QUALITY  # control signal, not quality