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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

Modelfile

@@ -0,0 +1,14 @@
+# ollama modelfile auto-generated by llamafactory
+
+FROM .
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+<|im_start|>assistant
+{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
+{{ end }}{{ end }}"""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096

README.md

@@ -1,3 +1,257 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- zh
+- en
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
+tags:
+- cybersecurity
+- security
+- network-security
+---
+# 🌎 SecGPT-14B-Reasoning  
+**让 SecGPT 具备深度思考的能力** | *Empowering SecGPT with Deep Reasoning Capabilities*
+
+---
+
+## 🧠 模型简介 | **Model Overview**
+SecGPT-14B-Reasoning 是基于 SecGPT-14B 的增强型网络安全大模型，首次引入 **动态推理模式切换** 能力，支持与 **Qwen3** 的 "思考-非思考模式" 无缝兼容。该模型通过强化逻辑推导与因果分析能力，在复杂安全场景中实现更精准的威胁研判与决策支持。
+
+
+**[SecGPT](https://huggingface.co/clouditera/secgpt)** 是由 **云起无垠** 于 2023 年正式推出的开源大模型，专为网络安全场景打造，旨在以人工智能技术全面提升安全防护效率与效果。
+
+
+> **中文**  
+> ✅ **动态模式**：自由切换 "深度推理" 与 "快速响应" 模式  
+> 🚀 **场景适配**：红队战术推演 / 蓝队威胁狩猎 / 自动化安全运营  
+> 🔍 **知识增强**：集成 CWE、CVE、ATT&CK 等权威安全知识库  
+
+> **English**  
+> ✅ **Dynamic Modes**: Switch between "Deep Reasoning" and "Fast Response" modes  
+> 🚀 **Scenario Adaptation**: Red Team tactics simulation / Blue Team threat hunting / Automated security operations  
+> 🔍 **Knowledge-Enhanced**: Integrated with authoritative databases like CWE, CVE, ATT&CK  
+
+
+### 启用思考模式 | Enable reasoning mode
+/think
+分析以下日志中的潜在 APT 攻击链特征。
+
+### 禁用思考模式 | Disable reasoning mode
+/no_think
+总结攻击链的关键节点。
+
+---
+
+## Use Cases
+
+`User:` SpringBoot常见的接口路由泄露有哪些？列举Swagger相关路径
+
+`Assistant:` <think>首先，我需要分析SpringBoot中常见的接口路由泄露问题，并特别关注与Swagger相关的路径。Swagger是一个流行的API文档工具，如果配置不当，可能会导致敏感信息泄露。接下来，我会列举出这些路径，并解释它们可能带来的安全风险。</think>
+
+在SpringBoot项目中，常见的接口路由泄露问题主要集中在Swagger相关路径上。以下是几个需要注意的路径：
+
+1. **/swagger-ui.html**：这是Swagger的默认UI界面，如果未进行访问控制，攻击者可以通过该路径直接查看API文档，了解系统的接口结构和潜在漏洞。
+
+2. **/api-docs**：这是Swagger生成的API文档的JSON格式输出，通常用于自动化测试或生成客户端代码。如果未进行保护，可能会泄露系统内部的详细接口信息。
+
+3. **/swagger-resources**：该路径返回Swagger资源列表，包括API定义文件的位置。如果未限制访问，攻击者可以获取到更多的系统信息。
+
+4. **/v2/api-docs**：这是Swagger API文档的另一种格式，类似于`/api-docs`，但通常更详细。同样，如果没有适当的保护措施，可能会导致敏感信息泄露。
+
+5. **/swagger.json**：这是Swagger的配置文件，包含API文档的相关信息。如果暴露在外，可能会被攻击者利用来进一步探测系统。
+
+为了防止这些路径的泄露，建议采取以下措施：
+- 禁用Swagger UI：通过配置禁用Swagger的Web UI。
+- 限制访问：使用Spring Security或其他安全框架对这些路径进行访问控制，确保只有授权用户才能访问。
+- 隐藏配置：避免将Swagger配置文件暴露在公开可访问的路径上。
+
+通过以上措施，可以有效减少接口路由泄露的风险，提高系统的安全性。
+
+
+`User:` 如何编写一个Suricata规则，用于检测png_handle_sPLT中潜在的整数溢出漏洞，特别是针对libPNG库中的分配过程，在HTTP端口的TCP流量中进行检测？
+
+`Assistant:` <think>为了编写一个有效的Suricata规则来检测png_handle_sPLT中的整数溢出漏洞，我们需要考虑以下几点：
+1. 确保规则能够捕捉到特定的HTTP流量（通常是80端口）。
+2. 检测特定的字符串模式，这些模式与libPNG库的sPLT处理相关。
+3. 使用正确的协议和流量方向来匹配HTTP流量。
+4. 确保规则能够准确识别并触发警报。</think>
+
+根据您的要求，我为您编写了一个Suricata规则，用于检测png_handle_sPLT中的整数溢出漏洞。以下是规则的具体内容：
+
+```
+alert tcp $HOME_NET any -> $EXTERNAL_NET 80 (msg:"ET EXPLOIT Potential PNG Integer Overflow in libPNG png_handle_sPLT"; flow:established,to_server; content:"|89 50 4E 47|"; depth:4; content:"|78 9C|"; distance:0; content:"|50 4C 54 53|"; within:16; content:"|00 00|"; distance:0; reference:url,www.securityfocus.com/bid/29931; classtype:attempted-user; sid:2015618; rev:1; metadata:created_at 2012_04_10, updated_at 2019_07_26;)
+```
+
+### 规则解释：
+
+1. **流量方向**：`alert tcp $HOME_NET any -> $EXTERNAL_NET 80` 表示我们正在监控从内部网络到外部网络的HTTP流量（通常使用80端口）。
+2. **消息说明**：`msg:"ET EXPLOIT Potential PNG Integer Overflow in libPNG png_handle_sPLT"` 是当规则匹配时将显示的消息，帮助安全人员快速理解警报的内容。
+3. **流量状态**：`flow:established,to_server` 确保只在已建立的连接且数据流向服务器时触发规则，避免误报。
+4. **PNG文件头检测**：`content:"|89 50 4E 47|"; depth:4` 检测PNG文件的魔数（PNG文件头），确保我们正在分析的确实是PNG文件。
+5. **压缩标识符检测**：`content:"|78 9C|"; distance:0` 检测PNG文件中的压缩标识符，进一步确认文件格式。
+6. **sPLT标记检测**：`content:"|50 4C 54 53|"; within:16` 检测sPLT标记，这是我们要重点关注的部分，因为整数溢出可能发生在处理sPLT数据时。
+7. **零值检测**：`content:"|00 00|"; distance:0` 检测零值，这可能是整数溢出的迹象。
+8. **参考链接**：`reference:url,www.securityfocus.com/bid/29931` 提供了关于此漏洞的更多信息，以便于进一步研究。
+9. **分类类型**：`classtype:attempted-user` 将此规则归类为用户尝试攻击，有助于优先级排序。
+10. **唯一标识符**：`sid:2015618; rev:1` 为规则提供唯一的标识符，并记录版本信息。
+11. **元数据**：`metadata:created_at 2012_04_10, updated_at 2019_07_26` 提供了规则创建和更新的时间信息，有助于跟踪其历史。
+
+通过这个规则，您可以有效地检测到png_handle_sPLT中的整数溢出漏洞，从而提高网络安全防护能力。
+
+
+`User:` 员工反映收到一封含有附件的邮件，声称是发票。附件是一个带有宏的Excel文件。
+
+`Assistant:` <think>员工收到的邮件可能涉及社会工程学攻击，通过伪装成合法发票来诱导用户打开恶意附件。这种攻击通常利用用户对发票的信任，通过Excel文件中的宏来执行恶意代码。需要详细分析邮件内容、附件类型和潜在风险，并提供相应的防护建议。</think>
+
+这封邮件很可能是一次钓鱼攻击，攻击者试图通过伪装成合法发票来诱使员工打开恶意附件。以下是详细分析及建议：
+
+1. **攻击手法**：
+   - 社会工程学攻击：攻击者利用员工对发票的信任，伪装邮件来源。
+   - 恶意附件：Excel文件中的宏可能包含恶意代码，用于窃取信息或安装恶意软件。
+
+2. **潜在风险**：
+   - 数据泄露：如果宏被激活，可能会窃取敏感数据。
+   - 系统感染：恶意代码可能导致系统被植入病毒或木马。
+   - 业务中断：攻击者可能通过获取权限进一步破坏业务流程。
+
+3. **防护措施**：
+   - **邮件过滤**：使用邮件安全工具过滤可疑邮件，阻止含有宏的附件。
+   - **员工培训**：教育员工识别钓鱼邮件，避免点击不明链接或附件。
+   - **禁用宏功能**：在组织中禁用Excel文件中的宏功能，减少风险。
+   - **定期更新**：确保所有软件和操作系统保持最新版本，修复已知漏洞。
+   - **多因素认证**：实施多因素认证，增加账户安全性。
+
+4. **应急响应**：
+   - 如果发现异常，立即报告给IT部门或安全团队。
+   - 隔离受影响的设备，防止攻击扩散。
+   - 分析日志，确定攻击范围和影响。
+
+通过以上措施，可以有效降低此类攻击的风险，保护企业和个人的信息安全。
+
+
+`User:` 介绍MITRE技术T1005
+
+`Assistant:` <think>首先，我需要确认MITRE ATT&CK框架中的技术T1005的具体内容。根据MITRE ATT&CK的描述，T1005主要涉及攻击者通过访问本地系统文件来收集信息的行为。接下来，我将详细解释该技术的工作原理、可能的利用方式以及相关的缓解措施。</think>
+
+MITRE ATT&CK技术T1005（本地系统数据）描述了攻击者如何通过访问本地系统文件来获取敏感信息的行为。这种技术通常用于收集以下类型的数据：
+
+1. **用户凭证**：包括用户名和密码等身份验证信息。
+2. **账户凭据**：例如登录会话令牌或API密钥。
+3. **配置文件**：可能包含数据库连接字符串或其他关键设置。
+4. **文档和报告**：可能包含机密信息或商业敏感数据。
+
+### 工作原理
+攻击者可以通过多种方式访问这些文件：
+- **直接访问文件**：例如读取存储在磁盘上的文件。
+- **使用系统工具**：如`cat`、`find`、`grep`等命令行工具。
+- **利用漏洞**：通过已知的系统或应用程序漏洞获取对文件的访问权限。
+
+### 潜在影响
+成功利用T1005可能导致以下后果：
+- 数据泄露：敏感信息被未经授权的第三方访问。
+- 身份盗用：攻击者可能冒充合法用户进行进一步攻击。
+- 系统破坏：攻击者可能利用获取的信息进行更深入的渗透。
+
+### 缓解措施
+为了降低T1005的风险，可以采取以下措施：
+1. **最小化权限**：限制用户和进程对敏感文件的访问权限���
+2. **加密敏感数据**：确保存储在本地的敏感信息经过加密处理。
+3. **监控文件访问**：使用安全工具监控异常的文件访问行为。
+4. **定期审计**：检查系统中是否存在未授权的文件访问记录。
+
+通过以上措施，可以有效减少攻击者利用T1005技术的风险。
+
+---
+
+## Quickstart
+
+The code of Qwen2.5 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
+
+With `transformers<4.37.0`, you will encounter the following error:
+```
+KeyError: 'qwen2'
+```
+
+The following contains a code snippet illustrating how to use the model generate content based on given inputs. 
+```python
+import torch
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "sds-ai/SecGPT-14B-Reasoning"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="cuda",
+    attn_implementation='flash_attention_2'
+)
+
+prompt = "SpringBoot常见的接口路由泄露有哪些？列举Swagger相关路径"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32768,
+    temperature=0.6
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+content = tokenizer.decode(output_ids, skip_special_tokens=True)
+
+print("content:", content)
+```
+
+## Switching Between Thinking and Non-Thinking Mode
+
+> [!TIP]
+> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM. 
+> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
+### `enable_thinking=True`
+
+By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
+
+```python
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=True  # True is the default value for enable_thinking
+)
+```
+
+In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
+
+> [!NOTE]
+> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
+### `enable_thinking=False`
+
+We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
+
+```python
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=False  # Setting enable_thinking=False disables thinking mode
+)
+```
+
+In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
+
+> [!NOTE]
+> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
+
+### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
+
+We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.

added_tokens.json

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+{
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+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

config.json

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "/home/models/secgpt",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.48.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.48.2"
+}

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model.safetensors.index.json

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+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}