diff --git a/hermes_cli/config.py b/hermes_cli/config.py
index 9c8bff52a39..eef366e1606 100644
--- a/hermes_cli/config.py
+++ b/hermes_cli/config.py
@@ -2382,7 +2382,7 @@ DEFAULT_CONFIG = {
         "surface": "auto",            # eligible surface: "auto" (first claimant) | "cli" | "tui" | "gui"
         "provider": "openwakeword",   # "openwakeword" (free, local) | "sherpa" (free, ANY phrase, no training) | "porcupine" (premium; needs PORCUPINE_ACCESS_KEY)
         "phrase": "hey hermes",       # for "sherpa" this IS the detected phrase (any text works); for other engines it's a cosmetic label — detection is keyed by the model/keyword below
-        "sensitivity": 0.5,           # 0.0-1.0 detection threshold (higher = stricter)
+        "sensitivity": 0.6,           # 0.0-1.0 detection threshold, consistent across engines (higher = stricter, fewer false triggers)
         "confirmation_frames": 3,     # openWakeWord only: consecutive over-threshold frames required to fire (higher = fewer false triggers on ambient speech, slightly more latency; 1 = old single-frame behavior)
         "start_new_session": True,    # start a fresh session on wake vs. continue the current one
         "profile_routing": True,      # sherpa only: also listen for every wake-enabled profile's phrase and route the wake to the matching profile
diff --git a/tests/tools/test_wake_word.py b/tests/tools/test_wake_word.py
index 83097328de3..baca95ab91b 100644
--- a/tests/tools/test_wake_word.py
+++ b/tests/tools/test_wake_word.py
@@ -27,7 +27,9 @@ def test_config_defaults_and_clamping():
     assert ww._provider({"provider": "Porcupine"}) == "porcupine"
     assert ww._sensitivity({"sensitivity": 5}) == 1.0
     assert ww._sensitivity({"sensitivity": -1}) == 0.0
-    assert ww._sensitivity({"sensitivity": "nope"}) == 0.5
+    # Invalid input falls back to the configured default, not a hardcoded 0.5.
+    assert ww._sensitivity({"sensitivity": "nope"}) == ww._DEFAULTS["sensitivity"]
+    assert ww._sensitivity({}) == ww._DEFAULTS["sensitivity"]
     assert ww.wake_phrase({"phrase": "hey hermes"}) == "hey hermes"
     assert ww.wake_phrase({}) == "hey hermes"
 
@@ -428,6 +430,44 @@ def test_confirmation_frames_config_clamped(monkeypatch):
     assert ww._confirmation_frames({}) == ww._DEFAULT_CONFIRMATION_FRAMES
 
 
+def test_porcupine_sensitivity_is_inverted_to_match_shared_contract(monkeypatch):
+    # Our config contract is "higher sensitivity = stricter" for every engine.
+    # Porcupine's own `sensitivities` param means the OPPOSITE (higher = looser,
+    # more false alarms), so the engine must pass 1 - sensitivity.
+    captured = {}
+
+    class _FakePorcupine:
+        frame_length = 512
+
+        def process(self, frame):
+            return -1
+
+    def _create(**kwargs):
+        captured.update(kwargs)
+        return _FakePorcupine()
+
+    pv = types.ModuleType("pvporcupine")
+    pv.create = _create
+    monkeypatch.setitem(sys.modules, "pvporcupine", pv)
+    monkeypatch.setattr("tools.lazy_deps.ensure", lambda *a, **k: None)
+    monkeypatch.setenv("PORCUPINE_ACCESS_KEY", "test-key")
+
+    # Strict (0.9) → Porcupine gets a low 0.1 (few false alarms).
+    ww._PorcupineEngine({"provider": "porcupine", "sensitivity": 0.9})
+    assert captured["sensitivities"] == [pytest.approx(0.1)]
+
+    # Loose (0.2) → Porcupine gets a high 0.8.
+    ww._PorcupineEngine({"provider": "porcupine", "sensitivity": 0.2})
+    assert captured["sensitivities"] == [pytest.approx(0.8)]
+
+
+def test_default_sensitivity_is_stricter_than_openwakeword_baseline():
+    # Regression: the 0.5 default let near-misses ("hey hor") through. The
+    # default must sit above openWakeWord's permissive 0.5 baseline.
+    assert ww._DEFAULTS["sensitivity"] >= 0.6
+    assert ww._sensitivity({}) >= 0.6
+
+
 def test_macos_tflite_refuses_silent_onnx_downgrade(monkeypatch):
     # openWakeWord silently falls back to onnx when no tflite runtime imports.
     # On macOS ARM64 that lands on the broken backend, so we must raise instead
diff --git a/tools/wake_word.py b/tools/wake_word.py
index 93de79501e3..9367127fe3f 100644
--- a/tools/wake_word.py
+++ b/tools/wake_word.py
@@ -78,7 +78,7 @@ _DEFAULTS: Dict[str, Any] = {
     "surface": "auto",
     "provider": "openwakeword",
     "phrase": "hey hermes",
-    "sensitivity": 0.5,
+    "sensitivity": 0.6,
     "confirmation_frames": _DEFAULT_CONFIRMATION_FRAMES,
     "start_new_session": True,
 }
@@ -170,7 +170,7 @@ def _sensitivity(cfg: Dict[str, Any]) -> float:
     try:
         s = float(raw)
     except (TypeError, ValueError):
-        s = 0.5
+        s = float(_DEFAULTS["sensitivity"])
     return min(max(s, 0.0), 1.0)
 
 
@@ -327,6 +327,10 @@ class _OpenWakeWordEngine(_Engine):
         framework = str(sub.get("inference_framework") or "").strip().lower()
         if not framework:
             framework = default_inference_framework()
+        # openWakeWord returns a 0..1 score per frame; sensitivity IS the raw
+        # threshold a score must clear. Higher = stricter (fewer false fires).
+        # Default 0.6 sits above openWakeWord's permissive 0.5 baseline, which
+        # let near-misses like "hey hor" through.
         self._threshold = _sensitivity(cfg)
         self._confirm_needed = _confirmation_frames(cfg)
         self._confirm_streak = 0
@@ -575,9 +579,13 @@ class _PorcupineEngine(_Engine):
 
         sub = cfg.get("porcupine") if isinstance(cfg.get("porcupine"), dict) else {}
         keyword = str(sub.get("keyword") or "jarvis").strip()
-        sensitivity = _sensitivity(cfg)
+        # Porcupine's `sensitivities` runs the OPPOSITE way to our shared knob:
+        # per Picovoice, higher = more true positives AND more false alarms
+        # (looser). Our config contract is "higher = stricter" everywhere, so
+        # invert it here to keep one consistent meaning across all engines.
+        porcupine_sensitivity = 1.0 - _sensitivity(cfg)
 
-        kwargs: Dict[str, Any] = {"access_key": access_key, "sensitivities": [sensitivity]}
+        kwargs: Dict[str, Any] = {"access_key": access_key, "sensitivities": [porcupine_sensitivity]}
         if _looks_like_path(keyword):
             kwargs["keyword_paths"] = [keyword]
         else:
diff --git a/website/docs/user-guide/features/wake-word.md b/website/docs/user-guide/features/wake-word.md
index 50d602406e7..ee57d9bf45d 100644
--- a/website/docs/user-guide/features/wake-word.md
+++ b/website/docs/user-guide/features/wake-word.md
@@ -76,7 +76,7 @@ wake_word:
   surface: auto               # eligible surface: "auto" | "cli" | "tui" | "gui"
   provider: openwakeword      # "openwakeword" (free, local) | "porcupine"
   phrase: "hey hermes"        # cosmetic label only — detection is keyed by the model/keyword below
-  sensitivity: 0.5            # 0.0-1.0 — raise to reduce false triggers
+  sensitivity: 0.6            # 0.0-1.0 — higher = stricter (fewer false triggers), consistent across all engines
   confirmation_frames: 3      # openWakeWord only — consecutive over-threshold frames required to fire
   start_new_session: true     # start a fresh session on wake vs. continue the current one
   openwakeword:
@@ -101,10 +101,18 @@ threshold and fire the wake word unintentionally. Two knobs control this:
   just one. Raise it (e.g. `4`–`5`) if you still get false triggers in a noisy
   room; the cost is a few tens of milliseconds of extra latency. `1` restores
   the old fire-on-first-frame behavior.
-- **`sensitivity`** — raise toward `1.0` to demand a higher score per frame.
+- **`sensitivity`** (default `0.6`) — the detection threshold, `0.0`–`1.0`.
+  Higher is stricter (fewer false triggers). This direction is consistent across
+  **all** engines — for openWakeWord it's the raw per-frame score threshold, for
+  sherpa it maps onto the keyword threshold, and for Porcupine it's inverted
+  internally so "higher = stricter" holds there too. The `0.6` default sits
+  above openWakeWord's permissive `0.5` baseline, which let near-misses like
+  "hey hor" through; raise toward `0.8` if you still get false fires, lower it
+  if real "hey hermes" utterances are missed.
 
 The `sherpa` and `porcupine` engines decode the whole phrase internally, so they
-don't have the single-frame-spike problem and ignore `confirmation_frames`.
+don't have the single-frame-spike problem and ignore `confirmation_frames`
+(but they still honor `sensitivity`).
 
 `inference_framework` picks the openWakeWord backend. Leave it empty (the
 default) to let Hermes choose per platform: **tflite on Apple Silicon**, onnx