fix: address multiple bugs found in code review
- Fix dirty-user flush race: discard IDs individually after successful save - Escape LIKE wildcards in LLM-generated topic keywords for DB queries - Anonymize absent-member aliases to prevent LLM de-anonymization - Pass correct MIME type to vision model based on image file extension - Use enumerate instead of list.index() in bcs-scan loop - Allow bot @mentions with non-report intent to fall through to moderation Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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@@ -273,11 +273,14 @@ class ChatCog(commands.Cog):
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image_attachment.filename,
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user_text[:80],
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)
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ext = image_attachment.filename.rsplit(".", 1)[-1].lower() if "." in image_attachment.filename else "png"
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mime = f"image/{'jpeg' if ext == 'jpg' else ext}"
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response = await self.bot.llm_heavy.analyze_image(
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image_bytes,
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IMAGE_ROAST,
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user_text=user_text,
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on_first_token=start_typing,
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media_type=mime,
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)
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else:
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# --- Text-only path: normal chat ---
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@@ -320,9 +320,8 @@ class CommandsCog(commands.Cog):
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f"Scanning {len(messages)} messages... (first request may be slow while model loads)"
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)
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for msg in messages:
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for idx, msg in enumerate(messages):
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# Build context from the messages before this one
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idx = messages.index(msg)
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ctx_msgs = messages[max(0, idx - 3):idx]
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context = (
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" | ".join(f"{m.author.display_name}: {m.content}" for m in ctx_msgs)
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@@ -145,7 +145,9 @@ class SentimentCog(commands.Cog):
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mention_config = config.get("mention_scan", {})
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if mention_config.get("enabled", True):
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await self._maybe_start_mention_scan(message, mention_config)
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return
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return
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# For non-report intents, fall through to buffer the message
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# so it still gets scored for toxicity
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# Skip if empty
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if not message.content or not message.content.strip():
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@@ -317,11 +319,13 @@ class SentimentCog(commands.Cog):
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if aliases:
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anon_key = anon_map.get(msg.author.display_name, msg.author.display_name)
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lines.append(f" {anon_key} is also known as: {', '.join(aliases)}")
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# Also include aliases for members NOT in the conversation (so the LLM
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# can recognize name-drops of absent members)
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# Include aliases for members NOT in the conversation (so the LLM
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# can recognize name-drops of absent members), using anonymized keys
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absent_idx = 0
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for uid, aliases in all_aliases.items():
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if uid not in seen_ids:
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lines.append(f" (not in chat) also known as: {', '.join(aliases)}")
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absent_idx += 1
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lines.append(f" Absent_{absent_idx} is also known as: {', '.join(aliases)}")
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return "\n".join(lines) if lines else ""
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@staticmethod
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@@ -32,19 +32,24 @@ async def flush_dirty_states(bot, dirty_users: set[int]) -> None:
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if not dirty_users:
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return
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dirty = list(dirty_users)
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dirty_users.clear()
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saved = 0
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for user_id in dirty:
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user_data = bot.drama_tracker.get_user(user_id)
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await bot.db.save_user_state(
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user_id=user_id,
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offense_count=user_data.offense_count,
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immune=user_data.immune,
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off_topic_count=user_data.off_topic_count,
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baseline_coherence=user_data.baseline_coherence,
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user_notes=user_data.notes or None,
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warned=user_data.warned_since_reset,
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last_offense_at=user_data.last_offense_time or None,
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aliases=_aliases_csv(user_data),
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warning_expires_at=user_data.warning_expires_at or None,
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)
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logger.info("Flushed %d dirty user states to DB.", len(dirty))
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try:
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await bot.db.save_user_state(
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user_id=user_id,
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offense_count=user_data.offense_count,
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immune=user_data.immune,
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off_topic_count=user_data.off_topic_count,
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baseline_coherence=user_data.baseline_coherence,
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user_notes=user_data.notes or None,
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warned=user_data.warned_since_reset,
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last_offense_at=user_data.last_offense_time or None,
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aliases=_aliases_csv(user_data),
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warning_expires_at=user_data.warning_expires_at or None,
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)
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dirty_users.discard(user_id)
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saved += 1
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except Exception:
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logger.exception("Failed to flush state for user %d", user_id)
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logger.info("Flushed %d/%d dirty user states to DB.", saved, len(dirty))
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