Initial commit: hardened DeerFlow factory

Vendored deer-flow upstream (bytedance/deer-flow) plus prompt-injection
hardening:

- New deerflow.security package: content_delimiter, html_cleaner,
  sanitizer (8 layers — invisible chars, control chars, symbols, NFC,
  PUA, tag chars, horizontal whitespace collapse with newline/tab
  preservation, length cap)
- New deerflow.community.searx package: web_search, web_fetch,
  image_search backed by a private SearX instance, every external
  string sanitized and wrapped in <<<EXTERNAL_UNTRUSTED_CONTENT>>>
  delimiters
- All native community web providers (ddg_search, tavily, exa,
  firecrawl, jina_ai, infoquest, image_search) replaced with hard-fail
  stubs that raise NativeWebToolDisabledError at import time, so a
  misconfigured tool.use path fails loud rather than silently falling
  back to unsanitized output
- Native client back-doors (jina_client.py, infoquest_client.py)
  stubbed too
- Native-tool tests quarantined under tests/_disabled_native/
  (collect_ignore_glob via local conftest.py)
- Sanitizer Layer 7 fix: only collapse horizontal whitespace, preserve
  newlines and tabs so list/table structure survives
- Hardened runtime config.yaml references only the searx-backed tools
- Factory overlay (backend/) kept in sync with deer-flow tree as a
  reference / source

See HARDENING.md for the full audit trail and verification steps.

deer-flow/backend/tests/test_patched_minimax.py

@@ -0,0 +1,149 @@
+from langchain_core.messages import AIMessageChunk, HumanMessage
+
+from deerflow.models.patched_minimax import PatchedChatMiniMax
+
+
+def _make_model(**kwargs) -> PatchedChatMiniMax:
+    return PatchedChatMiniMax(
+        model="MiniMax-M2.5",
+        api_key="test-key",
+        base_url="https://example.com/v1",
+        **kwargs,
+    )
+
+
+def test_get_request_payload_preserves_thinking_and_forces_reasoning_split():
+    model = _make_model(extra_body={"thinking": {"type": "disabled"}})
+
+    payload = model._get_request_payload([HumanMessage(content="hello")])
+
+    assert payload["extra_body"]["thinking"]["type"] == "disabled"
+    assert payload["extra_body"]["reasoning_split"] is True
+
+
+def test_create_chat_result_maps_reasoning_details_to_reasoning_content():
+    model = _make_model()
+    response = {
+        "choices": [
+            {
+                "message": {
+                    "role": "assistant",
+                    "content": "最终答案",
+                    "reasoning_details": [
+                        {
+                            "type": "reasoning.text",
+                            "id": "reasoning-text-1",
+                            "format": "MiniMax-response-v1",
+                            "index": 0,
+                            "text": "先分析问题，再给出答案。",
+                        }
+                    ],
+                },
+                "finish_reason": "stop",
+            }
+        ],
+        "model": "MiniMax-M2.5",
+    }
+
+    result = model._create_chat_result(response)
+    message = result.generations[0].message
+
+    assert message.content == "最终答案"
+    assert message.additional_kwargs["reasoning_content"] == "先分析问题，再给出答案。"
+    assert result.generations[0].text == "最终答案"
+
+
+def test_create_chat_result_strips_inline_think_tags():
+    model = _make_model()
+    response = {
+        "choices": [
+            {
+                "message": {
+                    "role": "assistant",
+                    "content": "<think>\n这是思考过程。\n</think>\n\n真正回答。",
+                },
+                "finish_reason": "stop",
+            }
+        ],
+        "model": "MiniMax-M2.5",
+    }
+
+    result = model._create_chat_result(response)
+    message = result.generations[0].message
+
+    assert message.content == "真正回答。"
+    assert message.additional_kwargs["reasoning_content"] == "这是思考过程。"
+    assert result.generations[0].text == "真正回答。"
+
+
+def test_convert_chunk_to_generation_chunk_preserves_reasoning_deltas():
+    model = _make_model()
+    first = model._convert_chunk_to_generation_chunk(
+        {
+            "choices": [
+                {
+                    "delta": {
+                        "role": "assistant",
+                        "content": "",
+                        "reasoning_details": [
+                            {
+                                "type": "reasoning.text",
+                                "id": "reasoning-text-1",
+                                "format": "MiniMax-response-v1",
+                                "index": 0,
+                                "text": "The user",
+                            }
+                        ],
+                    }
+                }
+            ]
+        },
+        AIMessageChunk,
+        {},
+    )
+    second = model._convert_chunk_to_generation_chunk(
+        {
+            "choices": [
+                {
+                    "delta": {
+                        "content": "",
+                        "reasoning_details": [
+                            {
+                                "type": "reasoning.text",
+                                "id": "reasoning-text-1",
+                                "format": "MiniMax-response-v1",
+                                "index": 0,
+                                "text": " asks.",
+                            }
+                        ],
+                    }
+                }
+            ]
+        },
+        AIMessageChunk,
+        {},
+    )
+    answer = model._convert_chunk_to_generation_chunk(
+        {
+            "choices": [
+                {
+                    "delta": {
+                        "content": "最终答案",
+                    },
+                    "finish_reason": "stop",
+                }
+            ],
+            "model": "MiniMax-M2.5",
+        },
+        AIMessageChunk,
+        {},
+    )
+
+    assert first is not None
+    assert second is not None
+    assert answer is not None
+
+    combined = first.message + second.message + answer.message
+
+    assert combined.additional_kwargs["reasoning_content"] == "The user asks."
+    assert combined.content == "最终答案"