glitext-class-edge / modelaudit.json
semioticrobotic's picture
Duplicate from rpeel/glitext-class-edge
94a67f2
{
"tool": "modelaudit",
"tool_version": "0.2.40",
"scanned_at": "2026-04-26T23:37:34Z",
"files": {
"model.onnx": {
"size_mb": 131.1,
"sha256": "5289497ae11bb612806e56eefa168c6338e3e5d9f548e8012f1e10560e19fa8f"
}
},
"audit": {
"bytes_scanned": 134726908,
"issues": [
{
"message": "Layer 'model.text_projector.linear_1.weight' has 1 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "model.text_projector.linear_1.weight",
"outlier_neurons": [
43
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
3.000617742538452
],
"weight_norms": [
0.5273075103759766
],
"mean_norm": 0.5908768773078918,
"std_norm": 0.02118542604148388,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.8968546,
"type": "onnx_check"
},
{
"message": "Layer 'model.text_projector.linear_2.weight' has 2 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "model.text_projector.linear_2.weight",
"outlier_neurons": [
214,
265
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
3.4721202850341797,
3.839141368865967
],
"weight_norms": [
0.6805276870727539,
0.6899271607398987
],
"mean_norm": 0.591606080532074,
"std_norm": 0.025610174983739853,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.8978963,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2032' has 4 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2032",
"outlier_neurons": [
730,
760,
762,
764
],
"total_outliers": 4,
"outlier_percentage": 0.3472222222222222,
"z_scores": [
3.254678249359131,
3.366765260696411,
3.440369129180908,
3.077902317047119
],
"weight_norms": [
3.0971879959106445,
3.1553280353546143,
3.1935067176818848,
3.005493402481079
],
"mean_norm": 1.4089704751968384,
"std_norm": 0.518704891204834,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.8985758,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2053' has 2 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2053",
"outlier_neurons": [
358,
363
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
6.91820764541626,
10.892086029052734
],
"weight_norms": [
1.986020803451538,
2.709052085876465
],
"mean_norm": 0.7272806167602539,
"std_norm": 0.18194599449634552,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.899232,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2055' has 3 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2055",
"outlier_neurons": [
77,
358,
363
],
"total_outliers": 3,
"outlier_percentage": 0.78125,
"z_scores": [
3.1322412490844727,
5.322834491729736,
6.233516693115234
],
"weight_norms": [
2.7848806381225586,
3.6990485191345215,
4.079090118408203
],
"mean_norm": 1.4777485132217407,
"std_norm": 0.417315274477005,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.899889,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2075' has 1 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2075",
"outlier_neurons": [
363
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
7.383401870727539
],
"weight_norms": [
1.4844307899475098
],
"mean_norm": 0.7127715945243835,
"std_norm": 0.10451269149780273,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.900539,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2076' has 6 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2076",
"outlier_neurons": [
27,
194,
456,
688,
770,
1095
],
"total_outliers": 6,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
3.035195827484131,
3.0513501167297363,
3.05649995803833,
3.1463961601257324,
7.347891330718994,
3.1944825649261475
],
"weight_norms": [
0.3287472128868103,
0.3244526982307434,
1.948188066482544,
1.9720864295959473,
3.089028835296631,
1.9848699569702148
],
"mean_norm": 1.1356358528137207,
"std_norm": 0.2658440172672272,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9011943,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2078' has 2 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2078",
"outlier_neurons": [
170,
486
],
"total_outliers": 2,
"outlier_percentage": 0.1736111111111111,
"z_scores": [
3.227228879928589,
3.220970392227173
],
"weight_norms": [
1.981728196144104,
1.9800732135772705
],
"mean_norm": 1.1282992362976074,
"std_norm": 0.2644463777542114,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9018571,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2098' has 4 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2098",
"outlier_neurons": [
67,
490,
573,
643
],
"total_outliers": 4,
"outlier_percentage": 0.3472222222222222,
"z_scores": [
10.147518157958984,
3.2569260597229004,
3.1233837604522705,
12.771944999694824
],
"weight_norms": [
4.698851108551025,
2.2743470668792725,
2.2273592948913574,
5.622274398803711
],
"mean_norm": 1.128374457359314,
"std_norm": 0.35185712575912476,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9025378,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2099' has 2 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2099",
"outlier_neurons": [
358,
363
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
7.9214091300964355,
10.449893951416016
],
"weight_norms": [
2.7937510013580322,
3.2416014671325684
],
"mean_norm": 1.3906946182250977,
"std_norm": 0.1771220713853836,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9031963,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2119' has 1 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2119",
"outlier_neurons": [
363
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
4.427882671356201
],
"weight_norms": [
1.6316750049591064
],
"mean_norm": 0.7824981808662415,
"std_norm": 0.19177943468093872,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9038618,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2121' has 2 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2121",
"outlier_neurons": [
358,
363
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
6.758997440338135,
13.026949882507324
],
"weight_norms": [
2.784144401550293,
4.112109184265137
],
"mean_norm": 1.3521441221237183,
"std_norm": 0.21186578273773193,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.904512,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2122' has 8 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2122",
"outlier_neurons": [
9,
266,
276,
279,
284,
285,
306,
554
],
"total_outliers": 8,
"outlier_percentage": 0.6944444444444444,
"z_scores": [
3.212179660797119,
3.245704412460327,
3.1307146549224854,
3.316779851913452,
3.2148783206939697,
3.2838239669799805,
3.1412272453308105,
3.164783477783203
],
"weight_norms": [
1.7544312477111816,
1.7613235712051392,
1.7376829385757446,
1.7759358882904053,
1.754986047744751,
1.769160509109497,
1.7398442029953003,
1.7446870803833008
],
"mean_norm": 1.0940423011779785,
"std_norm": 0.20558904111385345,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9051602,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2143' has 2 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2143",
"outlier_neurons": [
358,
363
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
6.859812259674072,
10.17564582824707
],
"weight_norms": [
2.7563867568969727,
3.4448232650756836
],
"mean_norm": 1.3321459293365479,
"std_norm": 0.20762096345424652,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9058108,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2144' has 6 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2144",
"outlier_neurons": [
67,
138,
140,
554,
556,
614
],
"total_outliers": 6,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
3.020602226257324,
3.1754822731018066,
3.826287031173706,
3.2961931228637695,
3.650977611541748,
3.5217597484588623
],
"weight_norms": [
0.3887397348880768,
1.7699042558670044,
1.9149746894836426,
1.796811819076538,
1.8758965730667114,
1.8470927476882935
],
"mean_norm": 1.0620598793029785,
"std_norm": 0.22290925681591034,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9065673,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2163' has 1 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2163",
"outlier_neurons": [
317
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
3.0252268314361572
],
"weight_norms": [
0.36917099356651306
],
"mean_norm": 1.0253673791885376,
"std_norm": 0.21690815687179565,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9072561,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2165' has 2 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2165",
"outlier_neurons": [
358,
363
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
3.6371476650238037,
7.814241886138916
],
"weight_norms": [
2.133096933364868,
3.1184256076812744
],
"mean_norm": 1.2751353979110718,
"std_norm": 0.2358885556459427,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.907795,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2166' has 4 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2166",
"outlier_neurons": [
217,
218,
219,
602
],
"total_outliers": 4,
"outlier_percentage": 0.3472222222222222,
"z_scores": [
3.0621910095214844,
3.151977300643921,
3.188354253768921,
3.0198326110839844
],
"weight_norms": [
1.8419299125671387,
1.868815302848816,
1.879707932472229,
1.829246163368225
],
"mean_norm": 0.924994170665741,
"std_norm": 0.2994377911090851,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.908296,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2185' has 2 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2185",
"outlier_neurons": [
141,
317
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
3.005260944366455,
3.345065116882324
],
"weight_norms": [
0.428337424993515,
0.36667728424072266
],
"mean_norm": 0.9736661911010742,
"std_norm": 0.1814580261707306,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.908806,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2186' has 5 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2186",
"outlier_neurons": [
252,
338,
828,
914,
946
],
"total_outliers": 5,
"outlier_percentage": 0.4340277777777778,
"z_scores": [
11.505152702331543,
4.314464569091797,
12.327495574951172,
9.297687530517578,
3.366377115249634
],
"weight_norms": [
4.306219577789307,
2.2771012783050537,
4.538273811340332,
3.6833016872406006,
2.009563446044922
],
"mean_norm": 1.0596158504486084,
"std_norm": 0.28218692541122437,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.909304,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2187' has 3 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2187",
"outlier_neurons": [
147,
358,
363
],
"total_outliers": 3,
"outlier_percentage": 0.78125,
"z_scores": [
3.2084083557128906,
3.8869893550872803,
9.003568649291992
],
"weight_norms": [
2.062852144241333,
2.2326014041900635,
3.5125303268432617
],
"mean_norm": 1.2602583169937134,
"std_norm": 0.2501532733440399,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9098086,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2188' has 3 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2188",
"outlier_neurons": [
326,
651,
742
],
"total_outliers": 3,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
3.1055855751037598,
3.0717461109161377,
3.0304436683654785
],
"weight_norms": [
1.6304196119308472,
1.6237001419067383,
1.6154987812042236
],
"mean_norm": 1.0137479305267334,
"std_norm": 0.19856856763362885,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9103193,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2207' has 1 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2207",
"outlier_neurons": [
141
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
3.137122869491577
],
"weight_norms": [
0.4656246602535248
],
"mean_norm": 1.142965316772461,
"std_norm": 0.215911403298378,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9108434,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2208' has 5 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2208",
"outlier_neurons": [
147,
489,
723,
731,
1111
],
"total_outliers": 5,
"outlier_percentage": 0.4340277777777778,
"z_scores": [
3.3600692749023438,
3.3963823318481445,
8.018890380859375,
3.6322925090789795,
3.479583501815796
],
"weight_norms": [
0.2726581394672394,
1.847255825996399,
2.9245359897613525,
1.9022349119186401,
1.8666459321975708
],
"mean_norm": 1.0557255744934082,
"std_norm": 0.23305098712444305,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9113624,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2209' has 2 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2209",
"outlier_neurons": [
358,
363
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
4.950748920440674,
6.248614311218262
],
"weight_norms": [
2.431596517562866,
2.7228353023529053
],
"mean_norm": 1.3206568956375122,
"std_norm": 0.22439830005168915,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.911881,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2230' has 7 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2230",
"outlier_neurons": [
252,
299,
715,
793,
798,
843,
853
],
"total_outliers": 7,
"outlier_percentage": 0.607638888888889,
"z_scores": [
3.6941657066345215,
3.1229217052459717,
3.9711530208587646,
3.111565589904785,
3.424854278564453,
3.259798765182495,
3.0462286472320557
],
"weight_norms": [
1.8310163021087646,
1.7133229970932007,
1.8880839347839355,
1.7109832763671875,
1.7755300998687744,
1.7415237426757812,
1.6975219249725342
],
"mean_norm": 1.0699079036712646,
"std_norm": 0.20602984726428986,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9123945,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2231' has 1 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2231",
"outlier_neurons": [
363
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
3.510470151901245
],
"weight_norms": [
2.2871758937835693
],
"mean_norm": 1.3612122535705566,
"std_norm": 0.26377198100090027,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.91292,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2252' has 7 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2252",
"outlier_neurons": [
316,
661,
736,
815,
839,
942,
1027
],
"total_outliers": 7,
"outlier_percentage": 0.607638888888889,
"z_scores": [
3.2526912689208984,
3.417073965072632,
3.306257486343384,
3.0526297092437744,
7.109450340270996,
7.056085586547852,
3.029418468475342
],
"weight_norms": [
1.8308838605880737,
1.868377447128296,
1.8431016206741333,
1.785252332687378,
2.710561990737915,
2.698390245437622,
1.7799581289291382
],
"mean_norm": 1.0889859199523926,
"std_norm": 0.2280874103307724,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9134305,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2255' has 1 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2255",
"outlier_neurons": [
244
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
3.056356430053711
],
"weight_norms": [
0.6583805680274963
],
"mean_norm": 0.5920038223266602,
"std_norm": 0.021717606112360954,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.913943,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2256' has 2 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2256",
"outlier_neurons": [
62,
268
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
3.048487663269043,
3.0232150554656982
],
"weight_norms": [
0.7006304264068604,
0.6999441981315613
],
"mean_norm": 0.617854654788971,
"std_norm": 0.02715306170284748,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.914449,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2257' has 2 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2257",
"outlier_neurons": [
206,
250
],
"total_outliers": 2,
"outlier_percentage": 0.78125,
"z_scores": [
3.219963312149048,
3.752326488494873
],
"weight_norms": [
0.9235143065452576,
0.9379115700721741
],
"mean_norm": 0.8364334106445312,
"std_norm": 0.027044065296649933,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9149594,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2258' has 1 output neurons with abnormal weight magnitudes",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2258",
"outlier_neurons": [
60
],
"total_outliers": 1,
"outlier_percentage": 0.78125,
"z_scores": [
3.0868079662323
],
"weight_norms": [
0.6551735401153564
],
"mean_norm": 0.5127619504928589,
"std_norm": 0.0461355522274971,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9154654,
"type": "onnx_check"
},
{
"message": "Layer 'onnx::MatMul_2259' has neurons with extremely large weight values",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"layer": "onnx::MatMul_2259",
"affected_neurons": [
0
],
"total_affected": 1,
"num_extreme_weights": 1,
"threshold": 0.08830882608890533,
"max_weight": 0.09035700559616089,
"total_outputs": 1,
"analysis_method": "structural_analysis"
},
"why": "Weight values that are orders of magnitude larger than typical can cause numerical instability, overflow attacks, or may encode hidden data. Detection uses statistical analysis rather than name-based classification to avoid security bypasses.",
"timestamp": 1777246651.9158247,
"type": "onnx_check",
"rule_code": "S802"
}
],
"checks": [
{
"name": "Path Exists",
"status": "passed",
"message": "Path exists",
"location": "/opt/sas/model-gli-text/models/class-edge/README.md",
"details": {
"path": "/opt/sas/model-gli-text/models/class-edge/README.md"
},
"timestamp": 1777246575.039381
},
{
"name": "Path Readable",
"status": "passed",
"message": "Path is readable",
"location": "/opt/sas/model-gli-text/models/class-edge/README.md",
"details": {
"path": "/opt/sas/model-gli-text/models/class-edge/README.md"
},
"timestamp": 1777246575.0394099
},
{
"name": "File Type Validation",
"status": "passed",
"message": "File type validation passed",
"location": "/opt/sas/model-gli-text/models/class-edge/README.md",
"details": {},
"timestamp": 1777246575.0394428
},
{
"name": "Path Exists",
"status": "passed",
"message": "Path exists",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"path": "/opt/sas/model-gli-text/models/class-edge/model.onnx"
},
"timestamp": 1777246575.0802407
},
{
"name": "Path Readable",
"status": "passed",
"message": "Path is readable",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"path": "/opt/sas/model-gli-text/models/class-edge/model.onnx"
},
"timestamp": 1777246575.0802708
},
{
"name": "File Type Validation",
"status": "passed",
"message": "File type validation passed",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {},
"timestamp": 1777246575.0802965
},
{
"name": "File Integrity Hash",
"status": "passed",
"message": "File integrity hashes calculated",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"md5": "20298350a5e7fe666dd69ced6481d792",
"sha256": "5289497ae11bb612806e56eefa168c6338e3e5d9f548e8012f1e10560e19fa8f",
"sha512": "709fd6baee90e7e7ab1b1cc502ffbbd2133d857211bdaacc74fc7fae76ba02e207af9e458c990873c59ac38fd9d8df090a4994a7e410d5a8d7228f994660d13c",
"file_size": 131130181
},
"timestamp": 1777246575.7523339
},
{
"name": "JIT/Script Code Execution Detection",
"status": "passed",
"message": "No JIT/Script code execution risks detected",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {},
"timestamp": 1777246649.262821
},
{
"name": "Network Communication Detection",
"status": "passed",
"message": "No network communication patterns detected",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {},
"timestamp": 1777246649.2629182
},
{
"name": "Custom Operator Domain Check",
"status": "passed",
"message": "All operators use standard ONNX domains",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"safe_nodes": 1694
},
"timestamp": 1777246649.2702723
},
{
"name": "Python Operator Detection",
"status": "passed",
"message": "No Python operators detected",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"nodes_checked": 1694
},
"timestamp": 1777246649.2702963
},
{
"name": "Tensor Size Validation",
"status": "passed",
"message": "Tensor Size Validation completed successfully",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx (tensor: model.text_projector.linear_1.weight)",
"details": {
"component_count": 76
},
"timestamp": 1777246649.4810627
},
{
"name": "Weight Distribution Anomaly Detection",
"status": "failed",
"message": "Weight Distribution Anomaly Detection found 33 issues",
"severity": "info",
"location": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"details": {
"component_count": 33,
"findings": [
{
"layer": "model.text_projector.linear_1.weight",
"outlier_neurons": [
43
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
3.000617742538452
],
"weight_norms": [
0.5273075103759766
],
"mean_norm": 0.5908768773078918,
"std_norm": 0.02118542604148388,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "model.text_projector.linear_2.weight",
"outlier_neurons": [
214,
265
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
3.4721202850341797,
3.839141368865967
],
"weight_norms": [
0.6805276870727539,
0.6899271607398987
],
"mean_norm": 0.591606080532074,
"std_norm": 0.025610174983739853,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2032",
"outlier_neurons": [
730,
760,
762,
764
],
"total_outliers": 4,
"outlier_percentage": 0.3472222222222222,
"z_scores": [
3.254678249359131,
3.366765260696411,
3.440369129180908,
3.077902317047119
],
"weight_norms": [
3.0971879959106445,
3.1553280353546143,
3.1935067176818848,
3.005493402481079
],
"mean_norm": 1.4089704751968384,
"std_norm": 0.518704891204834,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2053",
"outlier_neurons": [
358,
363
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
6.91820764541626,
10.892086029052734
],
"weight_norms": [
1.986020803451538,
2.709052085876465
],
"mean_norm": 0.7272806167602539,
"std_norm": 0.18194599449634552,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2055",
"outlier_neurons": [
77,
358,
363
],
"total_outliers": 3,
"outlier_percentage": 0.78125,
"z_scores": [
3.1322412490844727,
5.322834491729736,
6.233516693115234
],
"weight_norms": [
2.7848806381225586,
3.6990485191345215,
4.079090118408203
],
"mean_norm": 1.4777485132217407,
"std_norm": 0.417315274477005,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2075",
"outlier_neurons": [
363
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
7.383401870727539
],
"weight_norms": [
1.4844307899475098
],
"mean_norm": 0.7127715945243835,
"std_norm": 0.10451269149780273,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2076",
"outlier_neurons": [
27,
194,
456,
688,
770,
1095
],
"total_outliers": 6,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
3.035195827484131,
3.0513501167297363,
3.05649995803833,
3.1463961601257324,
7.347891330718994,
3.1944825649261475
],
"weight_norms": [
0.3287472128868103,
0.3244526982307434,
1.948188066482544,
1.9720864295959473,
3.089028835296631,
1.9848699569702148
],
"mean_norm": 1.1356358528137207,
"std_norm": 0.2658440172672272,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2078",
"outlier_neurons": [
170,
486
],
"total_outliers": 2,
"outlier_percentage": 0.1736111111111111,
"z_scores": [
3.227228879928589,
3.220970392227173
],
"weight_norms": [
1.981728196144104,
1.9800732135772705
],
"mean_norm": 1.1282992362976074,
"std_norm": 0.2644463777542114,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2098",
"outlier_neurons": [
67,
490,
573,
643
],
"total_outliers": 4,
"outlier_percentage": 0.3472222222222222,
"z_scores": [
10.147518157958984,
3.2569260597229004,
3.1233837604522705,
12.771944999694824
],
"weight_norms": [
4.698851108551025,
2.2743470668792725,
2.2273592948913574,
5.622274398803711
],
"mean_norm": 1.128374457359314,
"std_norm": 0.35185712575912476,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2099",
"outlier_neurons": [
358,
363
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
7.9214091300964355,
10.449893951416016
],
"weight_norms": [
2.7937510013580322,
3.2416014671325684
],
"mean_norm": 1.3906946182250977,
"std_norm": 0.1771220713853836,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2119",
"outlier_neurons": [
363
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
4.427882671356201
],
"weight_norms": [
1.6316750049591064
],
"mean_norm": 0.7824981808662415,
"std_norm": 0.19177943468093872,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2121",
"outlier_neurons": [
358,
363
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
6.758997440338135,
13.026949882507324
],
"weight_norms": [
2.784144401550293,
4.112109184265137
],
"mean_norm": 1.3521441221237183,
"std_norm": 0.21186578273773193,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2122",
"outlier_neurons": [
9,
266,
276,
279,
284,
285,
306,
554
],
"total_outliers": 8,
"outlier_percentage": 0.6944444444444444,
"z_scores": [
3.212179660797119,
3.245704412460327,
3.1307146549224854,
3.316779851913452,
3.2148783206939697,
3.2838239669799805,
3.1412272453308105,
3.164783477783203
],
"weight_norms": [
1.7544312477111816,
1.7613235712051392,
1.7376829385757446,
1.7759358882904053,
1.754986047744751,
1.769160509109497,
1.7398442029953003,
1.7446870803833008
],
"mean_norm": 1.0940423011779785,
"std_norm": 0.20558904111385345,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2143",
"outlier_neurons": [
358,
363
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
6.859812259674072,
10.17564582824707
],
"weight_norms": [
2.7563867568969727,
3.4448232650756836
],
"mean_norm": 1.3321459293365479,
"std_norm": 0.20762096345424652,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2144",
"outlier_neurons": [
67,
138,
140,
554,
556,
614
],
"total_outliers": 6,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
3.020602226257324,
3.1754822731018066,
3.826287031173706,
3.2961931228637695,
3.650977611541748,
3.5217597484588623
],
"weight_norms": [
0.3887397348880768,
1.7699042558670044,
1.9149746894836426,
1.796811819076538,
1.8758965730667114,
1.8470927476882935
],
"mean_norm": 1.0620598793029785,
"std_norm": 0.22290925681591034,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2163",
"outlier_neurons": [
317
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
3.0252268314361572
],
"weight_norms": [
0.36917099356651306
],
"mean_norm": 1.0253673791885376,
"std_norm": 0.21690815687179565,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2165",
"outlier_neurons": [
358,
363
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
3.6371476650238037,
7.814241886138916
],
"weight_norms": [
2.133096933364868,
3.1184256076812744
],
"mean_norm": 1.2751353979110718,
"std_norm": 0.2358885556459427,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2166",
"outlier_neurons": [
217,
218,
219,
602
],
"total_outliers": 4,
"outlier_percentage": 0.3472222222222222,
"z_scores": [
3.0621910095214844,
3.151977300643921,
3.188354253768921,
3.0198326110839844
],
"weight_norms": [
1.8419299125671387,
1.868815302848816,
1.879707932472229,
1.829246163368225
],
"mean_norm": 0.924994170665741,
"std_norm": 0.2994377911090851,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2185",
"outlier_neurons": [
141,
317
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
3.005260944366455,
3.345065116882324
],
"weight_norms": [
0.428337424993515,
0.36667728424072266
],
"mean_norm": 0.9736661911010742,
"std_norm": 0.1814580261707306,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2186",
"outlier_neurons": [
252,
338,
828,
914,
946
],
"total_outliers": 5,
"outlier_percentage": 0.4340277777777778,
"z_scores": [
11.505152702331543,
4.314464569091797,
12.327495574951172,
9.297687530517578,
3.366377115249634
],
"weight_norms": [
4.306219577789307,
2.2771012783050537,
4.538273811340332,
3.6833016872406006,
2.009563446044922
],
"mean_norm": 1.0596158504486084,
"std_norm": 0.28218692541122437,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2187",
"outlier_neurons": [
147,
358,
363
],
"total_outliers": 3,
"outlier_percentage": 0.78125,
"z_scores": [
3.2084083557128906,
3.8869893550872803,
9.003568649291992
],
"weight_norms": [
2.062852144241333,
2.2326014041900635,
3.5125303268432617
],
"mean_norm": 1.2602583169937134,
"std_norm": 0.2501532733440399,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2188",
"outlier_neurons": [
326,
651,
742
],
"total_outliers": 3,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
3.1055855751037598,
3.0717461109161377,
3.0304436683654785
],
"weight_norms": [
1.6304196119308472,
1.6237001419067383,
1.6154987812042236
],
"mean_norm": 1.0137479305267334,
"std_norm": 0.19856856763362885,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2207",
"outlier_neurons": [
141
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
3.137122869491577
],
"weight_norms": [
0.4656246602535248
],
"mean_norm": 1.142965316772461,
"std_norm": 0.215911403298378,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2208",
"outlier_neurons": [
147,
489,
723,
731,
1111
],
"total_outliers": 5,
"outlier_percentage": 0.4340277777777778,
"z_scores": [
3.3600692749023438,
3.3963823318481445,
8.018890380859375,
3.6322925090789795,
3.479583501815796
],
"weight_norms": [
0.2726581394672394,
1.847255825996399,
2.9245359897613525,
1.9022349119186401,
1.8666459321975708
],
"mean_norm": 1.0557255744934082,
"std_norm": 0.23305098712444305,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2209",
"outlier_neurons": [
358,
363
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
4.950748920440674,
6.248614311218262
],
"weight_norms": [
2.431596517562866,
2.7228353023529053
],
"mean_norm": 1.3206568956375122,
"std_norm": 0.22439830005168915,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2230",
"outlier_neurons": [
252,
299,
715,
793,
798,
843,
853
],
"total_outliers": 7,
"outlier_percentage": 0.607638888888889,
"z_scores": [
3.6941657066345215,
3.1229217052459717,
3.9711530208587646,
3.111565589904785,
3.424854278564453,
3.259798765182495,
3.0462286472320557
],
"weight_norms": [
1.8310163021087646,
1.7133229970932007,
1.8880839347839355,
1.7109832763671875,
1.7755300998687744,
1.7415237426757812,
1.6975219249725342
],
"mean_norm": 1.0699079036712646,
"std_norm": 0.20602984726428986,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2231",
"outlier_neurons": [
363
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
3.510470151901245
],
"weight_norms": [
2.2871758937835693
],
"mean_norm": 1.3612122535705566,
"std_norm": 0.26377198100090027,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2252",
"outlier_neurons": [
316,
661,
736,
815,
839,
942,
1027
],
"total_outliers": 7,
"outlier_percentage": 0.607638888888889,
"z_scores": [
3.2526912689208984,
3.417073965072632,
3.306257486343384,
3.0526297092437744,
7.109450340270996,
7.056085586547852,
3.029418468475342
],
"weight_norms": [
1.8308838605880737,
1.868377447128296,
1.8431016206741333,
1.785252332687378,
2.710561990737915,
2.698390245437622,
1.7799581289291382
],
"mean_norm": 1.0889859199523926,
"std_norm": 0.2280874103307724,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2255",
"outlier_neurons": [
244
],
"total_outliers": 1,
"outlier_percentage": 0.26041666666666663,
"z_scores": [
3.056356430053711
],
"weight_norms": [
0.6583805680274963
],
"mean_norm": 0.5920038223266602,
"std_norm": 0.021717606112360954,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2256",
"outlier_neurons": [
62,
268
],
"total_outliers": 2,
"outlier_percentage": 0.5208333333333333,
"z_scores": [
3.048487663269043,
3.0232150554656982
],
"weight_norms": [
0.7006304264068604,
0.6999441981315613
],
"mean_norm": 0.617854654788971,
"std_norm": 0.02715306170284748,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2257",
"outlier_neurons": [
206,
250
],
"total_outliers": 2,
"outlier_percentage": 0.78125,
"z_scores": [
3.219963312149048,
3.752326488494873
],
"weight_norms": [
0.9235143065452576,
0.9379115700721741
],
"mean_norm": 0.8364334106445312,
"std_norm": 0.027044065296649933,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2258",
"outlier_neurons": [
60
],
"total_outliers": 1,
"outlier_percentage": 0.78125,
"z_scores": [
3.0868079662323
],
"weight_norms": [
0.6551735401153564
],
"mean_norm": 0.5127619504928589,
"std_norm": 0.0461355522274971,
"analysis_method": "structural_analysis",
"architecture_confidence": 0.8999999999999999
},
{
"layer": "onnx::MatMul_2259",
"affected_neurons": [
0
],
"total_affected": 1,
"num_extreme_weights": 1,
"threshold": 0.08830882608890533,
"max_weight": 0.09035700559616089,
"total_outputs": 1,
"analysis_method": "structural_analysis"
}
]
},
"why": "Neurons with weight magnitudes significantly different from others in the same layer may indicate tampering, backdoors, or training anomalies. These outliers are flagged when their statistical z-score exceeds the threshold. Thresholds are adjusted based on structural analysis of the model architecture.",
"timestamp": 1777246651.9158192
},
{
"name": "Path Exists",
"status": "passed",
"message": "Path exists",
"location": "/opt/sas/model-gli-text/models/class-edge/tokenizer.json",
"details": {
"path": "/opt/sas/model-gli-text/models/class-edge/tokenizer.json"
},
"timestamp": 1777246652.934864
},
{
"name": "Path Readable",
"status": "passed",
"message": "Path is readable",
"location": "/opt/sas/model-gli-text/models/class-edge/tokenizer.json",
"details": {
"path": "/opt/sas/model-gli-text/models/class-edge/tokenizer.json"
},
"timestamp": 1777246652.934904
},
{
"name": "File Type Validation",
"status": "passed",
"message": "File type validation passed",
"location": "/opt/sas/model-gli-text/models/class-edge/tokenizer.json",
"details": {},
"timestamp": 1777246652.9349384
},
{
"name": "Template Extraction",
"status": "passed",
"message": "No Jinja2 templates found in file",
"location": "/opt/sas/model-gli-text/models/class-edge/tokenizer.json",
"details": {
"file_type": "tokenizer_config"
},
"timestamp": 1777246653.1820912
}
],
"files_scanned": 4,
"assets": [
{
"path": "/opt/sas/model-gli-text/models/class-edge/LICENSE",
"type": "unknown"
},
{
"path": "/opt/sas/model-gli-text/models/class-edge/README.md",
"type": "metadata",
"size": 1505
},
{
"path": "/opt/sas/model-gli-text/models/class-edge/model.onnx",
"type": "onnx",
"size": 131130181
},
{
"path": "/opt/sas/model-gli-text/models/class-edge/tokenizer.json",
"type": "jinja2_template",
"size": 3583864
}
],
"has_errors": false,
"scanner_names": [
"metadata",
"onnx",
"jinja2_template"
],
"file_metadata": {
"/opt/sas/model-gli-text/models/class-edge/LICENSE": {
"license_info": [
{
"spdx_id": "Apache-2.0",
"name": "Apache License 2.0",
"confidence": 0.8,
"source": "file_header",
"commercial_allowed": true
}
],
"copyright_notices": [],
"license_files_nearby": [
"/opt/sas/model-gli-text/models/class-edge/LICENSE"
],
"is_dataset": false,
"is_model": false,
"risk_score": 0.0,
"scan_timestamp": 1777246575.0347283,
"content_hash": "cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30"
},
"/opt/sas/model-gli-text/models/class-edge/README.md": {
"file_size": 1505,
"license_info": [
{
"spdx_id": "Apache-2.0",
"name": "Apache License 2.0",
"confidence": 0.8,
"source": "file_header",
"commercial_allowed": true
}
],
"copyright_notices": [],
"license_files_nearby": [
"/opt/sas/model-gli-text/models/class-edge/LICENSE"
],
"is_dataset": false,
"is_model": false,
"risk_score": 0.0,
"scan_timestamp": 1777246575.0542111,
"content_hash": "9cdf7eecd3159ae7da6debef8e872a6dd3c77624ef99b94c213979fe7175bcff"
},
"/opt/sas/model-gli-text/models/class-edge/model.onnx": {
"file_size": 131130181,
"file_hashes": {
"md5": "20298350a5e7fe666dd69ced6481d792",
"sha256": "5289497ae11bb612806e56eefa168c6338e3e5d9f548e8012f1e10560e19fa8f",
"sha512": "709fd6baee90e7e7ab1b1cc502ffbbd2133d857211bdaacc74fc7fae76ba02e207af9e458c990873c59ac38fd9d8df090a4994a7e410d5a8d7228f994660d13c"
},
"license_info": [],
"copyright_notices": [],
"license_files_nearby": [
"/opt/sas/model-gli-text/models/class-edge/LICENSE"
],
"is_dataset": false,
"is_model": true,
"risk_score": 0.0,
"scan_timestamp": 1777246652.0719705,
"ir_version": 7,
"producer_name": "pytorch",
"node_count": 1694,
"layers_analyzed": 48,
"anomalies_found": 33,
"content_hash": "5289497ae11bb612806e56eefa168c6338e3e5d9f548e8012f1e10560e19fa8f"
},
"/opt/sas/model-gli-text/models/class-edge/tokenizer.json": {
"file_size": 3583864,
"ml_context": {
"frameworks": {},
"overall_confidence": 0.0,
"is_ml_content": false,
"detected_patterns": [],
"optimization_hints": [],
"file_type": "tokenizer_config",
"is_tokenizer": true,
"confidence": 2
},
"license_info": [],
"copyright_notices": [],
"license_files_nearby": [
"/opt/sas/model-gli-text/models/class-edge/LICENSE"
],
"is_dataset": true,
"is_model": false,
"risk_score": 0.0,
"scan_timestamp": 1777246653.1862533,
"content_hash": "b6e09abceac75851eae141e7d51224a9da835c0c03684282fe2d5c42c745754f"
}
},
"content_hash": "c8e2d48cb9deffda2dc9d8958861a2dd2b3519601536517aca66248c99e80602",
"start_time": 1777246574.8321774,
"duration": 78.36124658584595,
"total_checks": 16,
"passed_checks": 16,
"failed_checks": 0,
"success": true
}
}