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edited reports to validate schema
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shubhobm committed Mar 13, 2023
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16 changes: 5 additions & 11 deletions reports/2022/AVID-2022-R0001.json
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"deployer": [
"HuggingFace"
],
"artifact": [
"artifacts": [
{
"type": "Model",
"name": "bert-base-uncased"
}
]
},
"problemtype": {
"class": "LLM evaluation",
"classof": "LLM Evaluation",
"type": "Detection",
"description": {
"lang": "eng",
Expand All @@ -32,7 +32,7 @@
"sensitive": "gender"
},
"detection_method": {
"class": "Significance test",
"type": "Significance Test",
"name": "Two-sample z test for proportions"
}
}
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"Ethics"
],
"sep_view": [
{
"id": "E0101",
"name": "Group Fairness"
}
"E0101: Group fairness"
],
"lifecycle_view": [
{
"id": "L05",
"stage": "Evaluation"
}
"L05: Evaluation"
],
"taxonomy_version": "0.1"
}
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16 changes: 5 additions & 11 deletions reports/2022/AVID-2022-R0002.json
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"deployer": [
"HuggingFace"
],
"artifact": [
"artifacts": [
{
"type": "Model",
"name": "xlm-roberta-base"
}
]
},
"problemtype": {
"class": "LLM evaluation",
"classof": "LLM Evaluation",
"type": "Detection",
"description": {
"lang": "eng",
Expand All @@ -32,7 +32,7 @@
"sensitive": "gender"
},
"detection_method": {
"class": "Significance test",
"type": "Significance Test",
"name": "Two-sample z test for proportions"
}
}
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"Ethics"
],
"sep_view": [
{
"id": "E0101",
"name": "Group Fairness"
}
"E0101: Group fairness"
],
"lifecycle_view": [
{
"id": "L05",
"stage": "Evaluation"
}
"L05: Evaluation"
],
"taxonomy_version": "0.1"
}
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18 changes: 6 additions & 12 deletions reports/2022/AVID-2022-R0003.json
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"deployer": [
"HuggingFace"
],
"artifact": [
"artifacts": [
{
"type": "Model",
"name": "bert-base-uncased"
Expand All @@ -25,7 +25,7 @@
]
},
"problemtype": {
"class": "LLM evaluation",
"classof": "LLM Evaluation",
"type": "Detection",
"description": {
"lang": "eng",
Expand All @@ -39,8 +39,8 @@
"measured": "Probability of predicting biased pronouns",
"sensitive": "gender"
},
"detection": {
"class": "Significance test",
"detection_method": {
"type": "Significance Test",
"name": "One-sample z test for mean"
}
}
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"Ethics"
],
"sep_view": [
{
"id": "E0101",
"name": "Group Fairness"
}
"E0101: Group fairness"
],
"lifecycle_view": [
{
"id": "L05",
"stage": "Evaluation"
}
"L05: Evaluation"
],
"taxonomy_version": "0.1"
}
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18 changes: 6 additions & 12 deletions reports/2022/AVID-2022-R0004.json
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"deployer": [
"HuggingFace"
],
"artifact": [
"artifacts": [
{
"type": "Model",
"name": "xlm-roberta-base"
Expand All @@ -25,7 +25,7 @@
]
},
"problemtype": {
"class": "LLM evaluation",
"classof": "LLM Evaluation",
"type": "Detection",
"description": {
"lang": "eng",
Expand All @@ -39,8 +39,8 @@
"measured": "Probability of predicting biased pronouns",
"sensitive": "gender"
},
"detection": {
"class": "Significance test",
"detection_method": {
"type": "Significance Test",
"name": "One-sample z test for mean"
}
}
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"Ethics"
],
"sep_view": [
{
"id": "E0101",
"name": "Group Fairness"
}
"E0101: Group fairness"
],
"lifecycle_view": [
{
"id": "L05",
"stage": "Evaluation"
}
"L05: Evaluation"
],
"taxonomy_version": "0.1"
}
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18 changes: 6 additions & 12 deletions reports/2022/AVID-2022-R0005.json
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"deployer": [
"HuggingFace"
],
"artifact": [
"artifacts": [
{
"type": "Model",
"name": "EleutherAI/gpt-neo-125M"
}
]
},
"problemtype": {
"class": "LLM evaluation",
"classof": "LLM Evaluation",
"type": "Detection",
"description": {
"lang": "eng",
Expand All @@ -33,8 +33,8 @@
"measured": "Toxicity score",
"sensitive": "religious_ideology"
},
"detection": {
"class": "Significance test",
"detection_method": {
"type": "Significance Test",
"name": "Anderson-Darling k-sample test"
}
}
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"Ethics"
],
"sep_view": [
{
"id": "E0101",
"name": "Group Fairness"
}
"E0101: Group fairness"
],
"lifecycle_view": [
{
"id": "L05",
"stage": "Evaluation"
}
"L05: Evaluation"
],
"taxonomy_version": "0.1"
}
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10 changes: 6 additions & 4 deletions reports/review/AML.CS0000.json
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"deployer": [
"Palo Alto Networks malware detection system"
],
"artifacts": {
"type": "system",
"name": "Palo Alto Networks malware detection system"
}
"artifacts": [
{
"type": "System",
"name": "Palo Alto Networks malware detection system"
}
]
},
"problemtype": {
"classof": "ATLAS Case Study",
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10 changes: 6 additions & 4 deletions reports/review/AML.CS0001.json
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"deployer": [
"Palo Alto Networks ML-based DGA detection module"
],
"artifacts": {
"type": "system",
"name": "Palo Alto Networks ML-based DGA detection module"
}
"artifacts": [
{
"type": "System",
"name": "Palo Alto Networks ML-based DGA detection module"
}
]
},
"problemtype": {
"classof": "ATLAS Case Study",
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20 changes: 12 additions & 8 deletions reports/review/AML.CS0002.json
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"deployer": [
"VirusTotal"
],
"artifacts": {
"type": "system",
"name": "VirusTotal"
}
"artifacts": [
{
"type": "System",
"name": "VirusTotal"
}
]
},
"problemtype": {
"classof": "ATLAS Case Study",
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"value": "McAfee Advanced Threat Research noticed an increase in reports of a certain ransomware family that was out of the ordinary. Case investigation revealed that many samples of that particular ransomware family were submitted through a popular virus-sharing platform within a short amount of time. Further investigation revealed that based on string similarity the samples were all equivalent, and based on code similarity they were between 98 and 74 percent similar. Interestingly enough, the compile time was the same for all the samples. After more digging, researchers discovered that someone used 'metame' a metamorphic code manipulating tool to manipulate the original file towards mutant variants. The variants would not always be executable, but are still classified as the same ransomware family."
},
"impact": null,
"credit": {
"lang": "eng",
"value": "McAfee Advanced Threat Research"
},
"credit": [
{
"lang": "eng",
"value": "McAfee Advanced Threat Research"
}
],
"reported_date": "2020-01-01"
}
10 changes: 6 additions & 4 deletions reports/review/AML.CS0003.json
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"deployer": [
"CylancePROTECT, Cylance Smart Antivirus"
],
"artifacts": {
"type": "system",
"name": "CylancePROTECT, Cylance Smart Antivirus"
}
"artifacts": [
{
"type": "System",
"name": "CylancePROTECT, Cylance Smart Antivirus"
}
]
},
"problemtype": {
"classof": "ATLAS Case Study",
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20 changes: 12 additions & 8 deletions reports/review/AML.CS0004.json
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"deployer": [
"Shanghai government tax office's facial recognition service"
],
"artifacts": {
"type": "system",
"name": "Shanghai government tax office's facial recognition service"
}
"artifacts": [
{
"type": "System",
"name": "Shanghai government tax office's facial recognition service"
}
]
},
"problemtype": {
"classof": "ATLAS Case Study",
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"value": "This type of camera hijack attack can evade the traditional live facial recognition authentication model and enable access to privileged systems and victim impersonation.\n\nTwo individuals in China used this attack to gain access to the local government's tax system. They created a fake shell company and sent invoices via tax system to supposed clients. The individuals started this scheme in 2018 and were able to fraudulently collect $77 million.\n"
},
"impact": null,
"credit": {
"lang": "eng",
"value": "Ant Group AISEC Team"
},
"credit": [
{
"lang": "eng",
"value": "Ant Group AISEC Team"
}
],
"reported_date": "2020-01-01"
}
10 changes: 6 additions & 4 deletions reports/review/AML.CS0005.json
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"deployer": [
"Google Translate, Bing Translator, Systran Translate"
],
"artifacts": {
"type": "system",
"name": "Google Translate, Bing Translator, Systran Translate"
}
"artifacts": [
{
"type": "System",
"name": "Google Translate, Bing Translator, Systran Translate"
}
]
},
"problemtype": {
"classof": "ATLAS Case Study",
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10 changes: 6 additions & 4 deletions reports/review/AML.CS0006.json
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"deployer": [
"Clearview AI facial recognition tool"
],
"artifacts": {
"type": "system",
"name": "Clearview AI facial recognition tool"
}
"artifacts": [
{
"type": "System",
"name": "Clearview AI facial recognition tool"
}
]
},
"problemtype": {
"classof": "ATLAS Case Study",
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