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177 lines
8.2 KiB
JSON
177 lines
8.2 KiB
JSON
[
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{
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"purpose": "llm:structured_cn",
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"provider": "ollama-local",
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"endpoint": "http://127.0.0.1:11434",
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"model": "qwen3:0.6b",
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"request": {
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"model": "qwen3:0.6b",
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"messages": [
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{
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"role": "system",
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"content": "You detect sensitive information in logs. Return exact substrings only.\nTypes include cn_id_card, cn_phone, us_ssn, credit_card, password, secret,\naddress, medical_record, medical_diagnosis, treatment, passport, bank_account,\nrouting_number, bearer_token, email, private_key, and api_key. Distinguish a\nreal disclosed value from a field name, instruction, product code, metric, or\nexplicit decoy. start/end are zero-based Python slice offsets and confidence is\n0..1. Return JSON matching the schema; never redact or paraphrase the value."
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},
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{
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"role": "user",
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"content": "客户身份证号11010519491231002X，手机号13800138000。"
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}
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],
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"format": {
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"type": "object",
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"properties": {
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"findings": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"type": {
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"type": "string"
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},
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"value": {
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"type": "string"
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},
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"start": {
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"type": "integer"
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},
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"end": {
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"type": "integer"
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},
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"confidence": {
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"type": "number"
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}
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},
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"required": [
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"type",
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"value",
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"start",
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"end",
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"confidence"
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]
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}
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}
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},
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"required": [
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"findings"
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]
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},
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"options": {
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"temperature": 0,
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"seed": 37,
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"num_predict": 1200
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},
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"stream": false
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},
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"response": {
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"model": "qwen3:0.6b",
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"created_at": "2026-07-29T18:19:20.944483Z",
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"done": true,
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"done_reason": "stop",
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"total_duration": 3957502042,
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"load_duration": 57011625,
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"prompt_eval_count": 445,
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"prompt_eval_duration": 63544667,
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"eval_count": 76,
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"eval_duration": 335304839,
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"message": {
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"role": "assistant",
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"content": "{\"findings\":[{\"type\":\"secret\",\"value\":\"11010519491231002X\",\"start\":0,\"end\":18,\"confidence\":1},{\"type\":\"secret\",\"value\":\"13800138000\",\"start\":0,\"end\":11,\"confidence\":1}]}",
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"thinking": "好的，我现在需要处理用户提供的客户信息，并检查是否有敏感数据。用户给出的客户身份证号是11010519491231002X，手机号是13800138000。首先，我要确认这些数据是否符合敏感信息的定义。\n\n根据用户提供的类型，敏感信息包括身份证号、手机号等。身份证号有18位，而手机号是11位，但用户提供的都是18位和11位，这可能是一个错误，因为通常身份证号是18位，而手机号通常是11位。不过用户可能输入时有误，或者需要确认是否正确。同样，手机号是13800138000，共11位，这可能是一个错误，但用户可能希望保留。\n\n接下来，我需要检查是否有其他可能的字段或类型被提及，但用户的信息中只有这两个部分。因此，正确的做法是返回这两个部分的值，但需要确保它们是敏感信息。例如，身份证号和手机号都是敏感信息，因此应该直接返回它们的值，而无需额外处理。\n\n最后，确保返回的JSON格式正确，没有其他字段或错误信息，并且只包含敏感数据。这样用户可以清楚知道信息是否被泄露。\n",
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"images": null,
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"tool_calls": null
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}
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},
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"latency_ms": 3959.616,
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"usage": {
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"prompt_tokens": 445,
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"completion_tokens": 76
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}
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},
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{
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"purpose": "hybrid-after-regex:structured_cn",
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"provider": "ollama-local",
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"endpoint": "http://127.0.0.1:11434",
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"model": "qwen3:0.6b",
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"request": {
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"model": "qwen3:0.6b",
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"messages": [
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{
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"role": "system",
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"content": "You detect sensitive information in logs. Return exact substrings only.\nTypes include cn_id_card, cn_phone, us_ssn, credit_card, password, secret,\naddress, medical_record, medical_diagnosis, treatment, passport, bank_account,\nrouting_number, bearer_token, email, private_key, and api_key. Distinguish a\nreal disclosed value from a field name, instruction, product code, metric, or\nexplicit decoy. start/end are zero-based Python slice offsets and confidence is\n0..1. Return JSON matching the schema; never redact or paraphrase the value."
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},
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{
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"role": "user",
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"content": "客户身份证号11010519491231002X，手机号[REDACTED_CN_PHONE]。"
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}
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],
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"format": {
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"type": "object",
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"properties": {
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"findings": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"type": {
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"type": "string"
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},
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"value": {
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"type": "string"
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},
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"start": {
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"type": "integer"
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},
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"end": {
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"type": "integer"
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},
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"confidence": {
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"type": "number"
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}
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},
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"required": [
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"type",
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"value",
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"start",
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"end",
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"confidence"
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]
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}
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}
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},
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"required": [
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"findings"
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]
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},
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"options": {
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"temperature": 0,
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"seed": 37,
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"num_predict": 1200
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},
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"stream": false
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},
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"response": {
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"model": "qwen3:0.6b",
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"created_at": "2026-07-29T18:19:25.232685Z",
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"done": true,
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"done_reason": "stop",
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"total_duration": 4285037500,
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"load_duration": 58636292,
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"prompt_eval_count": 565,
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"prompt_eval_duration": 80994959,
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"eval_count": 75,
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"eval_duration": 335223796,
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"message": {
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"role": "assistant",
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"content": "{\"findings\":[{\"type\":\"cn_id_card\",\"value\":\"11010519491231002X\",\"start\":0,\"end\":18,\"confidence\":1},{\"type\":\"cn_phone\",\"value\":\"[REDACTED_CN_PHONE]\",\"start\":0,\"end\":0,\"confidence\":1}]}",
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"thinking": "好的，我现在需要处理用户提供的敏感信息，并按照要求返回精确的子字符串。用户给出的客户身份证号是11010519491231002X，手机号是[REDACTED_CN_PHONE]。首先，我要确认用户的需求，他们希望返回的值是身份证号和手机号中的敏感字段，但不包括其他信息。\n\n首先，身份证号的长度是18位，而用户提供的身份证号是11010519491231002X，这明显超过了标准的18位，所以可能用户输入时有误，或者存在其他问题。不过根据用户的要求，只要提取出身份证号和手机号中的敏感字段，不管是否正确，都要返回。因此，我需要检查身份证号中的敏感字段，比如可能的身份证号字段，但用户提供的身份证号是11010519491231002X，其中包含的字符是11010519491231002X，这可能是一个身份证号的前几位，但需要确认是否符合标准。不过根据用户的要求，只要提取出这些字段，不管是否正确，所以直接返回身份证号中的敏感部分，即11010519491231002X，手机号中的[REDACTED_CN_PHONE]，但需要确保没有其他字段被包含。\n\n接下来，检查手机号中的[REDACTED_CN_PHONE]，这里需要确认是否正确，但用户已经标记为[REDACTED_CN_PHONE]，所以直接返回这个部分。同时，确保不包含其他字段，比如地址、医疗记录等，只保留身份证号和手机号中的敏感信息。因此，最终的输出应该是包含身份证号和手机号的字段，但不包含其他信息。\n",
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"images": null,
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"tool_calls": null
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}
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},
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"latency_ms": 4287.042,
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"usage": {
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"prompt_tokens": 565,
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"completion_tokens": 75
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}
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}
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]
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