Add profanity in display and usernames, fix Docker setup

backend/routes/account.py

@@ -16,6 +16,7 @@ from validation import is_valid_password, is_valid_username, is_valid_display_na
 import os
 
 from security.audit import log_security
+from security.profanity import contains_profanity
 router = APIRouter()
 
 _FAILED_ATTEMPT_WINDOW_SECONDS = 300
@@ -185,12 +186,22 @@ def register(request: RegisterRequest, http: Request, db: Session = Depends(get_
             status_code=status.HTTP_400_BAD_REQUEST,
             detail="Имя пользователя должно быть от 3 до 20 символов и содержать только английские буквы, цифры, дефисы и подчеркивания"
         )
+    if contains_profanity(username):
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Имя пользователя содержит запрещённые слова"
+        )
 
     if not is_valid_display_name(display_name):
         raise HTTPException(
             status_code=status.HTTP_400_BAD_REQUEST,
             detail="Отображаемое имя должно быть от 1 до 64 символов и не может быть пустым"
         )
+    if contains_profanity(display_name):
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Отображаемое имя содержит запрещённые слова"
+        )
 
     if not is_valid_password(password):
         raise HTTPException(

backend/routes/messaging.py

@@ -7,6 +7,7 @@ import re
 import uuid
 import asyncio
 import time
+import unicodedata
 from collections import defaultdict, deque
 from difflib import SequenceMatcher
 from types import SimpleNamespace
@@ -44,22 +45,51 @@ _SPAM_SIMILARITY_THRESHOLD = 0.88
 _SPAM_MESSAGE_LIMIT = 5
 _BURST_WINDOW_SECONDS = 30
 _BURST_COUNT_THRESHOLD = 20
+_SHORT_MESSAGE_LENGTH = 8
+_SHORT_MESSAGE_REPEAT_LIMIT = 4
 
-_recent_message_cache: dict[int, deque[tuple[str, float]]] = defaultdict(deque)
+_recent_message_cache: dict[int, deque[tuple[str, str, float]]] = defaultdict(deque)
 _message_rate_cache: dict[int, deque[float]] = defaultdict(deque)
 _burst_last_logged: dict[int, float] = {}
 
 
+def _normalize_for_spam(text: str) -> str:
+    normalized = unicodedata.normalize("NFKC", text or "").casefold()
+    # Remove whitespace and punctuation while keeping alphanumerics
+    cleaned = re.sub(r"[^0-9a-zа-яё]+", "", normalized, flags=re.IGNORECASE)
+    return cleaned
+
+
 def _monitor_public_message_activity(user: User, content: str, db: Session) -> None:
     now = time.time()
 
+    def suspend(reason: str, event: str, **extra: Any) -> None:
+        if user.suspended or user.id == 1:
+            return
+        user.suspended = True
+        user.suspension_reason = reason
+        db.commit()
+        log_security(
+            event,
+            severity="warning",
+            user_id=user.id,
+            username=user.username,
+            reason=reason,
+            **extra,
+        )
+        try:
+            asyncio.create_task(messagingManager.send_suspension_to_user(user.id, reason))
+        except Exception:
+            pass
+
     # Rate tracking for burst detection
     rate_bucket = _message_rate_cache[user.id]
     rate_bucket.append(now)
     while rate_bucket and now - rate_bucket[0] > _BURST_WINDOW_SECONDS:
         rate_bucket.popleft()
 
-    if len(rate_bucket) >= _BURST_COUNT_THRESHOLD:
+    burst_count = len(rate_bucket)
+    if burst_count >= _BURST_COUNT_THRESHOLD:
         last_logged = _burst_last_logged.get(user.id)
         if not last_logged or now - last_logged > _BURST_WINDOW_SECONDS:
             log_security(
@@ -67,39 +97,52 @@ def _monitor_public_message_activity(user: User, content: str, db: Session) -> N
                 severity="warning",
                 user_id=user.id,
                 username=user.username,
-                count=len(rate_bucket),
+                count=burst_count,
                 window_seconds=_BURST_WINDOW_SECONDS,
             )
             _burst_last_logged[user.id] = now
+        suspend(
+            "Automatic suspension: excessive message rate",
+            "auto_suspension_public_burst",
+            count=burst_count,
+            window_seconds=_BURST_WINDOW_SECONDS,
+        )
 
     # Similarity-based spam detection
+    normalized = _normalize_for_spam(content)
     history = _recent_message_cache[user.id]
-    history.append((content, now))
-    while history and now - history[0][1] > _SPAM_WINDOW_SECONDS:
+    while history and now - history[0][2] > _SPAM_WINDOW_SECONDS:
         history.popleft()
 
-    similar_messages = sum(
-        1 for previous_content, _ in history
-        if SequenceMatcher(None, content, previous_content).ratio() >= _SPAM_SIMILARITY_THRESHOLD
+    prior_same = sum(1 for prev_norm, _, _ in history if prev_norm == normalized)
+    prior_similar = sum(
+        1
+        for prev_norm, _, _ in history
+        if prev_norm and normalized and prev_norm != normalized and SequenceMatcher(None, normalized, prev_norm).ratio() >= _SPAM_SIMILARITY_THRESHOLD
     )
 
-    if similar_messages >= _SPAM_MESSAGE_LIMIT and not user.suspended and user.id != 1:
-        reason = "Automatic suspension: repeated similar public messages"
-        user.suspended = True
-        user.suspension_reason = reason
-        db.commit()
-        log_security(
+    history.append((normalized, content, now))
+
+    total_matches = prior_same + prior_similar + 1
+
+    if len(normalized) <= _SHORT_MESSAGE_LENGTH and prior_same + 1 >= _SHORT_MESSAGE_REPEAT_LIMIT:
+        suspend(
+            "Automatic suspension: repeated short messages",
             "auto_suspension_public_spam",
-            severity="warning",
-            user_id=user.id,
-            username=user.username,
-            similar_messages=similar_messages,
+            occurrences=prior_same + 1,
             window_seconds=_SPAM_WINDOW_SECONDS,
+            match_type="short",
+        )
+        return
+
+    if total_matches >= _SPAM_MESSAGE_LIMIT:
+        suspend(
+            "Automatic suspension: repeated similar public messages",
+            "auto_suspension_public_spam",
+            similar_messages=total_matches,
+            window_seconds=_SPAM_WINDOW_SECONDS,
+            match_type="similar",
         )
-        try:
-            asyncio.create_task(messagingManager.send_suspension_to_user(user.id, reason))
-        except Exception:
-            pass
 
 
 def convert_message(msg: Message) -> dict:

backend/routes/profile.py

@@ -15,6 +15,7 @@ from validation import is_valid_username, is_valid_display_name
 from similarity import is_user_similar_to_verified
 from .messaging import messagingManager
 from security.audit import log_security
+from security.profanity import contains_profanity
 
 router = APIRouter()
 
@@ -180,6 +181,11 @@ async def update_user_profile(
                 status_code=400, 
                 detail="Имя пользователя должно быть от 3 до 20 символов и содержать только английские буквы, цифры, дефисы и подчеркивания"
             )
+        if contains_profanity(username):
+            raise HTTPException(
+                status_code=400,
+                detail="Имя пользователя содержит запрещённые слова"
+            )
         
         # Check if username is already taken by another user
         existing_user = db.query(User).filter(User.username == username, User.id != current_user.id).first()
@@ -197,6 +203,11 @@ async def update_user_profile(
                 status_code=400, 
                 detail="Отображаемое имя должно быть от 1 до 64 символов и не может быть пустым"
             )
+        if contains_profanity(display_name):
+            raise HTTPException(
+                status_code=400,
+                detail="Отображаемое имя содержит запрещённые слова"
+            )
         
         current_user.display_name = display_name
         updated = True

backend/security/audit.py

@@ -145,10 +145,28 @@ def _render_security(action: str, fields: Dict[str, Any]) -> List[str]:
     if action == "auto_suspension_public_spam":
         lines = [
             f"Automatic suspension triggered for {_format_user(fields)}",
-            f"Similar messages detected: {fields.get('similar_messages')}",
         ]
+        match_type = fields.get("match_type")
+        if match_type:
+            lines.append(f"Match type: {match_type}")
+        similar = fields.get("similar_messages")
+        occurrences = fields.get("occurrences")
+        if similar:
+            lines.append(f"Similar messages detected: {similar}")
+        if occurrences and not similar:
+            lines.append(f"Occurrences: {occurrences}")
         if fields.get("window_seconds"):
             lines.append(f"Observation window: {fields['window_seconds']} seconds")
+        if fields.get("reason"):
+            lines.append(f"Reason: {fields['reason']}")
+        return lines
+    if action == "auto_suspension_public_burst":
+        lines = [
+            f"Automatic suspension triggered for {_format_user(fields)}",
+            f"Messages sent: {fields.get('count')} within {fields.get('window_seconds')} seconds",
+        ]
+        if fields.get("reason"):
+            lines.append(f"Reason: {fields['reason']}")
         return lines
     if action == "public_message_burst":
         return [

backend/security/profanity.py

@@ -15,7 +15,9 @@ _CUSTOM_RU_TERMS: Set[str] = {
     "бляд", "блять", "бля", "сука", "суки", "сучка", "мразь", "ебан",
     "ебать", "ебёт", "ебет", "уёбок", "уебок", "уебище", "пизда",
     "пиздец", "пизд", "хуй", "хуя", "хуе", "хуё", "хер", "гондон",
-    "долбоёб", "долбоеб", "дебил", "член", "проститутка", "урод",
+    "долбоёб", "долбоеб", "дебил", "член", "проститутка", "проститутки",
+    "урод", "хуесос", "хуесосы", "хуесосов", "хуесоса", "пидор",
+    "пидоры", "пидорас", "пидорасы", "пидорасов",
 }
 
 _ADULT_TERMS: Set[str] = {
@@ -33,8 +35,167 @@ _PHRASE_PATTERNS: Tuple[re.Pattern[str], ...] = (
     re.compile(r"\bфромчат\s+г[ао]вно\b", re.IGNORECASE | re.UNICODE),
     re.compile(r"\b18\+\b", re.IGNORECASE | re.UNICODE),
     re.compile(r"\bxxx\b", re.IGNORECASE | re.UNICODE),
+    re.compile(r"\bайфон\s+топ\b", re.IGNORECASE | re.UNICODE),
+    re.compile(r"\bсамсунг\s+г[ао]вно\b", re.IGNORECASE | re.UNICODE),
 )
 
+_LEET_MAP = {
+    "0": "о",
+    "o": "о",
+    "о": "о",
+    "a": "а",
+    "@": "а",
+    "4": "а",
+    "а": "а",
+    "e": "е",
+    "ё": "е",
+    "3": "е",
+    "c": "с",
+    "s": "с",
+    "с": "с",
+    "x": "х",
+    "х": "х",
+    "t": "т",
+    "т": "т",
+    "p": "п",
+    "п": "п",
+    "n": "н",
+    "н": "н",
+    "m": "м",
+    "м": "м",
+    "y": "у",
+    "u": "у",
+    "у": "у",
+    "g": "г",
+    "г": "г",
+    "v": "в",
+    "в": "в",
+    "f": "ф",
+    "ф": "ф",
+    "i": "и",
+    "1": "и",
+    "и": "и",
+}
+
+_RAW_PHRASE_GROUPS: Tuple[Tuple[str, Tuple[str, ...]], ...] = (
+    ("generic", ("айфон", "топ")),
+    ("generic", ("самсунг", "говно")),
+)
+
+_SENSITIVE_PHRASE_PATH = Path("data/profanity/sensitive_phrases.json")
+_PHRASE_CACHE: dict[str, Tuple[Tuple[str, ...], ...]] = {}
+
+
+def _normalize_char(ch: str) -> str:
+    lower = ch.lower()
+    return _LEET_MAP.get(lower, lower)
+
+
+def _normalize_token(token: str) -> str:
+    return "".join(_normalize_char(ch) for ch in token)
+
+
+def _tokenize_with_spans(text: str) -> List[Tuple[int, int, str]]:
+    tokens: List[Tuple[int, int, str]] = []
+    start: int | None = None
+    buffer: List[str] = []
+
+    for idx, ch in enumerate(text):
+        if ch.isalnum() or ch in {"@", "#", "_"}:
+            if start is None:
+                start = idx
+            buffer.append(ch)
+        else:
+            if buffer and start is not None:
+                token_raw = "".join(buffer)
+                tokens.append((start, idx, _normalize_token(token_raw)))
+                buffer.clear()
+                start = None
+    if buffer and start is not None:
+        token_raw = "".join(buffer)
+        tokens.append((start, len(text), _normalize_token(token_raw)))
+    return tokens
+
+
+def _edit_distance_limited(a: str, b: str, max_distance: int = 1) -> bool:
+    if a == b:
+        return True
+    if max_distance <= 0:
+        return False
+    if abs(len(a) - len(b)) > max_distance:
+        return False
+
+    previous = list(range(len(b) + 1))
+    for i, ca in enumerate(a, 1):
+        current = [i]
+        best = current[0]
+        for j, cb in enumerate(b, 1):
+            insert_cost = current[j - 1] + 1
+            delete_cost = previous[j] + 1
+            replace_cost = previous[j - 1] + (0 if ca == cb else 1)
+            cost = min(insert_cost, delete_cost, replace_cost)
+            current.append(cost)
+            if cost < best:
+                best = cost
+        if best > max_distance:
+            return False
+        previous = current
+    return previous[-1] <= max_distance
+
+
+def _load_sensitive_phrases() -> List[Tuple[str, ...]]:
+    if not _SENSITIVE_PHRASE_PATH.exists():
+        return []
+    try:
+        payload = json.loads(_SENSITIVE_PHRASE_PATH.read_text(encoding="utf-8"))
+        phrases: List[Tuple[str, ...]] = []
+        if isinstance(payload, list):
+            for entry in payload:
+                if isinstance(entry, list) and entry:
+                    normalized = tuple(str(part).strip() for part in entry if str(part).strip())
+                    if normalized:
+                        phrases.append(normalized)
+        return phrases
+    except Exception:
+        return []
+
+
+def _get_phrases(group: str) -> Tuple[Tuple[str, ...], ...]:
+    if group not in _PHRASE_CACHE:
+        base = [phrase for key, phrase in _RAW_PHRASE_GROUPS if key == group]
+        if group == "sensitive":
+            base.extend(_load_sensitive_phrases())
+        _PHRASE_CACHE[group] = tuple(
+            tuple(_normalize_token(part) for part in phrase)
+            for phrase in base
+        )
+    return _PHRASE_CACHE[group]
+
+
+def _find_fuzzy_phrase_spans(text: str, group: str = "generic") -> List[Tuple[int, int]]:
+    tokens = _tokenize_with_spans(text)
+    if not tokens:
+        return []
+
+    spans: List[Tuple[int, int]] = []
+    normalized_phrases = _get_phrases(group)
+
+    for index in range(len(tokens)):
+        for phrase in normalized_phrases:
+            if index + len(phrase) > len(tokens):
+                continue
+            matches = True
+            for offset, target in enumerate(phrase):
+                token = tokens[index + offset][2]
+                if not _edit_distance_limited(token, target):
+                    matches = False
+                    break
+            if matches:
+                span_start = tokens[index][0]
+                span_end = tokens[index + len(phrase) - 1][1]
+                spans.append((span_start, span_end))
+    return spans
+
 _dictionary_lock = RLock()
 _blocklist_signature: Tuple[str, ...] | None = None
 _profanity = Profanity()
@@ -96,7 +257,11 @@ def _apply_phrase_filters(text: str) -> str:
             match = pattern.search(result)
             if not match:
                 break
-            result = result[:match.start()] + ("\\*" * (match.end() - match.start())) + result[match.end():]
+            result = result[:match.start()] + ("*" * (match.end() - match.start())) + result[match.end():]
+
+    for start, end in sorted(_find_fuzzy_phrase_spans(text, "generic"), reverse=True):
+        result = result[:start] + ("*" * (end - start)) + result[end:]
+
     return result
 
 
@@ -109,6 +274,27 @@ def censor_text(text: str) -> str:
     return _profanity.censor(preprocessed, censor_char="\\*")
 
 
+def contains_profanity(text: str) -> bool:
+    if not text:
+        return False
+
+    _rebuild_dictionary()
+    for pattern in _PHRASE_PATTERNS:
+        if pattern.search(text):
+            return True
+    if _find_fuzzy_phrase_spans(text, "generic"):
+        return True
+    return _profanity.contains_profanity(text)
+
+
+def contains_sensitive_phrase(text: str) -> bool:
+    if not text:
+        return False
+    if _find_fuzzy_phrase_spans(text, "sensitive"):
+        return True
+    return False
+
+
 def get_blocklist() -> List[str]:
     with _dictionary_lock:
         return sorted(_load_blocklist())

deployment/.dockerignore

@@ -30,4 +30,5 @@ coverage
 test_results/
 out
 
-data
+data
+logs