diff --git a/agent/input_sanitizer.py b/agent/input_sanitizer.py
index 806f3d53c..6fd8735fa 100644
--- a/agent/input_sanitizer.py
+++ b/agent/input_sanitizer.py
@@ -497,7 +497,7 @@ class InputSanitizer:
             ("combining_chars", r"[\u0300-\u036f\u1dc0-\u1dff]{5,}", 0.75),
             ("lookalike_digits", r"[𝟎𝟏𝟐𝟑𝟒𝟓𝟔𝟕𝟖𝟗𝟢𝟣𝟤𝟥𝟦𝟧𝟨𝟩𝟪𝟫0123456789]{10,}", 0.60),
             ("fullwidth_chars", r"[\uff01-\uff5e\uff10-\uff19]{10,}", 0.70),  # Fullwidth ASCII
-            ("mathematical_chars", r"[\u1d400-\u1d7ff]{5,}", 0.72),  # Mathematical alphanumeric
+            ("mathematical_chars", r"[\U0001D400-\U0001D7FF]{5,}", 0.72),  # Mathematical alphanumeric
             ("circled_chars", r"[\u2460-\u24ff\u24b6-\u24e9\u3200-\u32ff]{5,}", 0.68),
         ]
     
@@ -574,6 +574,46 @@ class InputSanitizer:
                 pass
         return None
     
+    def _should_skip_pattern(self, text: str, inj_type: InjectionType, pattern_name: str) -> bool:
+        """Fast-path rejection for expensive patterns that can't match given input.
+        
+        This prevents catastrophic backtracking on long inputs by skipping patterns
+        whose preconditions (length, keyword presence, etc.) are not met.
+        """
+        if inj_type == InjectionType.CONTEXT_FLOODING:
+            text_len = len(text)
+            if pattern_name == "repetition_flood":
+                if text_len < 2000 or text_len > 50000:
+                    return True
+            if pattern_name == "padding_attack" and text_len < 2000:
+                return True
+            if pattern_name == "nonsense_flood":
+                if text_len < 2000 or text_len > 50000:
+                    return True
+            if pattern_name == "garbage_suffix":
+                if text_len < 5000 or text_len > 50000:
+                    return True
+                lower = text.lower()
+                if not ("ignore" in lower or "forget" in lower or "disregard" in lower):
+                    return True
+                if not ("above" in lower or "previous" in lower):
+                    return True
+            if pattern_name == "filler_injection" and text_len < 500:
+                return True
+            if pattern_name == "unicode_noise" and text_len < 200:
+                return True
+            if pattern_name == "base64_noise" and text_len < 2000:
+                return True
+            if pattern_name == "nested_brackets" and text_len < 200:
+                return True
+            if pattern_name == "fake_history" and text_len < 500:
+                return True
+            if pattern_name == "lorem_ipsum":
+                lower = text.lower()
+                if "lorem ipsum" not in lower and "dolor sit amet" not in lower:
+                    return True
+        return False
+
     def analyze(self, text: str) -> List[InjectionMatch]:
         """Analyze text for injection patterns.
         
@@ -588,6 +628,8 @@ class InputSanitizer:
         # Check all compiled patterns
         for inj_type, pattern_list in self._compiled_patterns.items():
             for name, compiled_pattern, confidence in pattern_list:
+                if self._should_skip_pattern(text, inj_type, name):
+                    continue
                 for match in compiled_pattern.finditer(text):
                     matches.append(InjectionMatch(
                         injection_type=inj_type,