diff --git a/pyrit/converter/puzzled/keyword_masker.py b/pyrit/converter/puzzled/keyword_masker.py
index a14fd5a557..a8bf310e96 100644
--- a/pyrit/converter/puzzled/keyword_masker.py
+++ b/pyrit/converter/puzzled/keyword_masker.py
@@ -248,7 +248,13 @@ def _get_nlp() -> Any:
 
 def _pos_lookup(prompt: str) -> dict[str, str]:
     """
-    Map each alphabetic noun/verb in the prompt (by ``_word_key``) to its clue label.
+    Map each noun/verb in the prompt (by ``_word_key``) to its clue label.
+
+    The prompt is normalized before tagging because the tagger is sensitive to the spelling
+    it receives: composed "café" and decomposed "cafe\u0301" are given different parts of
+    speech, which would make the same prompt select a different word. Words are accepted with
+    ``_MASKABLE_WORD_PATTERN``, the same test ``_iter_words`` uses, so a word carrying a
+    combining mark keeps its tag; ``token.is_alpha`` is false for all of them.
 
     Returns an empty mapping when spaCy is unavailable.
 
@@ -262,8 +268,8 @@ def _pos_lookup(prompt: str) -> dict[str, str]:
     if nlp is None:
         return {}
     lookup: dict[str, str] = {}
-    for token in nlp(prompt):
-        if token.is_alpha and token.pos_ in _MASKABLE_POS:
+    for token in nlp(unicodedata.normalize("NFC", prompt)):
+        if _MASKABLE_WORD_PATTERN.fullmatch(token.text) and token.pos_ in _MASKABLE_POS:
             # Keep the first tag seen for a given surface form.
             lookup.setdefault(_word_key(token.text), _MASKABLE_POS[token.pos_])
     return lookup
diff --git a/tests/unit/converter/test_puzzled_keyword_masker.py b/tests/unit/converter/test_puzzled_keyword_masker.py
index bbbaf4d824..fc0bfc2530 100644
--- a/tests/unit/converter/test_puzzled_keyword_masker.py
+++ b/tests/unit/converter/test_puzzled_keyword_masker.py
@@ -45,6 +45,22 @@ def __call__(self, text: str) -> list[_FakeToken]:
         return self._tokens
 
 
+class _RecordingNLP:
+    """Records the text it is given and tags by exact spelling, the way the real tagger does.
+
+    ``is_alpha`` is derived with ``str.isalpha`` exactly as spaCy derives it, so a word
+    carrying a combining mark reports False here too.
+    """
+
+    def __init__(self, tags: dict[str, str]):
+        self.seen: list[str] = []
+        self._tags = tags
+
+    def __call__(self, text: str) -> list[_FakeToken]:
+        self.seen.append(text)
+        return [_FakeToken(word, self._tags.get(word, "ADV"), is_alpha=word.isalpha()) for word in text.split()]
+
+
 # --- mask count rule -------------------------------------------------------
 
 
@@ -147,6 +163,38 @@ def test_mask_prompt_prefers_pos_tagged_words_over_plain_words():
     assert {w.text for w in result.masked_words} == {"steal", "documents"}
 
 
+def test_pos_lookup_tags_words_with_combining_marks():
+    # is_alpha comes from str.isalpha, which is False once a combining mark is present, so
+    # the tag the pipeline produced would otherwise be dropped. Built at runtime so an editor
+    # that normalizes the file to NFC cannot silently change the test.
+    nfd = unicodedata.normalize("NFD", "café")
+    fake = _FakeNLP([_FakeToken(nfd, "NOUN", is_alpha=False)])
+    with patch.object(keyword_masker, "_get_nlp", lambda: fake):
+        assert keyword_masker._pos_lookup(nfd) == {keyword_masker._word_key(nfd): "noun"}
+
+
+def test_pos_lookup_normalizes_before_tagging():
+    nfd = unicodedata.normalize("NFD", "café")
+    fake = _RecordingNLP({})
+    with patch.object(keyword_masker, "_get_nlp", lambda: fake):
+        keyword_masker._pos_lookup(nfd)
+    assert fake.seen == [unicodedata.normalize("NFC", nfd)]
+
+
+def test_mask_prompt_selection_is_stable_across_normalization_forms():
+    # The fake tags "café" only in its composed spelling, which is how the real tagger
+    # behaves: it gives the decomposed spelling a different part of speech, and the tier in
+    # _rank_candidates then picks a different word.
+    selections = {}
+    for form in ("NFC", "NFD"):
+        fake = _RecordingNLP({"café": "NOUN"})
+        with patch.object(keyword_masker, "_get_nlp", lambda nlp=fake: nlp):
+            result = mask_prompt(unicodedata.normalize(form, "surprisingly café"), num_to_mask=1)
+        word = result.masked_words[0]
+        selections[form] = (unicodedata.normalize("NFC", word.text), word.clue)
+    assert selections["NFC"] == selections["NFD"] == ("café", "4-letter noun")
+
+
 @_no_spacy
 def test_mask_prompt_prefers_longer_words_without_hints():
     result = mask_prompt("cat elephant dog", num_to_mask=1)