Product Google Tensorflow

Timeline

The analysis of the timeline helps to identify the required approach and handling of single vulnerabilities and vulnerability collections. This overview makes it possible to see less important slices and more severe hotspots at a glance. Initiating immediate vulnerability response and prioritizing of issues is possible.

Last Year

The analysis of the timeline helps to identify the required approach and handling of single vulnerabilities and vulnerability collections. This overview makes it possible to see less important slices and more severe hotspots at a glance. Initiating immediate vulnerability response and prioritizing of issues is possible.

Version »

Grouping all affected versions of a specific product helps to determine existing issues. This makes it possible to determine vendors and products which need attention when it comes to remediations.

Remediation »

Vendors and researchers are eager to find countermeasures to mitigate security vulnerabilities. These can be distinguished between multiple forms and levels of remediation which influence risks differently.

Exploitability »

Researcher and attacker which are looking for security vulnerabilities try to exploit them for academic purposes or personal gain. The level and quality of exploitability can be distinguished to determine simplicity and strength of attacks.

Access Vector »

The approach a vulnerability it becomes important to use the expected access vector. This is typically via the network, local, or physically even.

Authentication »

To exploit a vulnerability a certail level of authentication might be required. Vulnerabilities without such a requirement are much more popular.

User Interaction »

Some attack scenarios require some user interaction by a victim. This is typical for phishing, social engineering and cross site scripting attacks.

C3BM Index »

Our unique C3BM Index (CVSSv3 Base Meta Index) cumulates the CVSSv3 Meta Base Scores of all entries over time. Comparing this index to the amount of disclosed vulnerabilities helps to pinpoint the most important events.

Last Year »

Our unique C3BM Index (CVSSv3 Base Meta Index) cumulates the CVSSv3 Meta Base Scores of all entries over time. Comparing this index to the amount of disclosed vulnerabilities helps to pinpoint the most important events.

CVSSv3 Base »

The Common Vulnerability Scoring System (CVSS) is an industry standard to define the characteristics and impacts of security vulnerabilities. The base score represents the intrinsic aspects that are constant over time and across user environments. Our unique meta score merges all available scores from different sources to aggregate to the most reliable result.

CVSSv3 Temp »

The Common Vulnerability Scoring System (CVSS) uses temp scores to reflect the characteristics of a vulnerability that may change over time but not across user environments. This includes reporting confidence, exploitability and remediation levels. We do also provide our unique meta score for temp scores, even though other sources rarely publish them.

VulDB »

The moderation team is always defining the base vector and base score for an entry. These and all other available scores are used to generate the meta score.

NVD »

The National Vulnerability Database (NVD) is also defining CVSS vectors and scores. These are usually not complete and might differ from VulDB scores.

Vendor »

Some vendors are willing to publish their own CVSS vectors and scores for vulnerabilities in their products. The coverage varies from vendor to vendor.

Research »

There are sometimes also security researcher which provide their own CVSS vectors and scores for vulnerabilities they have found and published.

Exploit 0-day »

The moderation team is working with the threat intelligence team to determine prices for exploits. Our unique algorithm is used to identify the 0-day prices for an exploit, before it got distributed or became public. Calculated prices are aligned to prices disclosed by vulnerability broker and compared to prices we see on exploit markets.

Exploit Today »

The 0-day prices do not consider time-relevant factors. The today price does reflect price impacts like disclosure of vulnerability details, alternative exploits, availability of countermeasures. These dynamic aspects might decrease the exploit prices over time. Under certain circumstances this happens very fast.

Exploit Market Volume »

Our unique calculation of exploit prices makes it possible to forecast the expected exploit market volume. The calculated prices for all possible 0-day expoits are cumulated for this task. Comparing the volume to the amount of disclosed vulnerabilities helps to pinpoint the most important events.

Last Year »

Our unique calculation of exploit prices makes it possible to forecast the expected exploit market volume. The calculated prices for all possible 0-day expoits are cumulated for this task. Comparing the volume to the amount of disclosed vulnerabilities helps to pinpoint the most important events.

🔴 CTI Activities »

Our unique Cyber Threat Intelligence aims to determine the ongoing research of actors to anticipiate their acitivities. Observing exploit markets on the Darknet, discussions of vulnerabilities on mailinglists, and exchanges on social media makes it possible to identify planned attacks. Monitored actors and activities are classified whether they are offensive or defensive. They are also weighted as some actors are well-known for certain products and technologies. And some of their disclosures might contain more or less details about technical aspects and personal context. The world map highlights active actors in real-time.

Affected Versions (27): 1.0, 1.1, 1.1.1, 1.1.2, 1.1.3, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.12, 1.12.1, 2.0, 2.1, 2.1.1, 2.1.2, 2.1.3, 2.2, 2.2.1, 2.2.2, 2.3, 2.3.1, 2.3.2, 2.4, 2.4.1, 2.5

Link to Product Website: https://www.google.com/

Software Type: Artificial Intelligence Software

PublishedBaseTempVulnerability0dayTodayExpRemCTICVE
06/30/20214.64.5Google TensorFlow Archive tf.keras.utils.get_file unknown vulnerability$5k-$25k$5k-$25kNot DefinedNot Defined1.84CVE-2021-35958
05/15/20214.94.7Google TensorFlow tf.transpose exceptional condition$5k-$25k$0-$5kNot DefinedOfficial Fix0.06CVE-2021-29618
05/15/20214.94.7Google TensorFlow tf.strings.substr exceptional condition$5k-$25k$0-$5kNot DefinedOfficial Fix0.00CVE-2021-29617
05/15/20214.34.1Google TensorFlow attr_value_util.cc ParseAttrValue recursion$5k-$25k$0-$5kNot DefinedOfficial Fix0.00CVE-2021-29615
05/15/20216.36.0Google TensorFlow tf.raw_ops.CTCLoss initialization$5k-$25k$5k-$25kNot DefinedOfficial Fix0.00CVE-2021-29613
05/15/20216.36.0Google TensorFlow common.c TFLiteIntArray integer overflow$5k-$25k$5k-$25kNot DefinedOfficial Fix0.00CVE-2021-29605
05/15/20214.34.1Google TensorFlow tf.raw_ops.IRFFT assertion$5k-$25k$0-$5kNot DefinedOfficial Fix0.00CVE-2021-29562
05/15/20214.34.1Google TensorFlow FPE Runtime tf.raw_ops.SparseMatMul divide by zero$5k-$25k$0-$5kNot DefinedOfficial Fix0.00CVE-2021-29557
05/15/20214.34.1Google TensorFlow tf.raw_ops.SparseCountSparseOutput exceptional condition$5k-$25k$0-$5kNot DefinedOfficial Fix0.00CVE-2021-29619
05/15/20216.36.0Google TensorFlow banded_triangular_solve_op.cc tf.raw_ops.BandedTriangularSolve buffer overflow$5k-$25k$5k-$25kNot DefinedOfficial Fix0.00CVE-2021-29612
05/15/20216.36.0Google TensorFlow sparse_reshape_op.cc SparseReshape initialization$5k-$25k$5k-$25kNot DefinedOfficial Fix0.00CVE-2021-29611
05/15/20217.06.7Google TensorFlow quantize_and_dequantize_op.cc tf.raw_ops.QuantizeAndDequantizeV2 initialization$5k-$25k$5k-$25kNot DefinedOfficial Fix0.00CVE-2021-29610
05/15/20216.36.0Google TensorFlow sparse_add_op.cc initialization$5k-$25k$5k-$25kNot DefinedOfficial Fix0.00CVE-2021-29609
05/15/20217.06.7Google TensorFlow ragged_tensor_to_tensor_op.cc tf.raw_ops.RaggedTensorToTensor heap-based overflow$5k-$25k$5k-$25kNot DefinedOfficial Fix0.00CVE-2021-29608
05/15/20216.36.0Google TensorFlow sparse_sparse_binary_op_shared.cc SparseAdd unusual condition$5k-$25k$0-$5kNot DefinedOfficial Fix0.00CVE-2021-29607
05/15/20216.36.0Google TensorFlow TFLite Model split_v.cc Split_V out-of-bounds read$5k-$25k$0-$5kNot DefinedOfficial Fix0.00CVE-2021-29606
05/15/20214.34.1Google TensorFlow hashtable_lookup.cc divide by zero$5k-$25k$0-$5kNot DefinedOfficial Fix0.05CVE-2021-29604
05/15/20216.36.0Google TensorFlow TFLite Model arg_min_max.cc ArgMax out-of-bounds write$5k-$25k$5k-$25kNot DefinedOfficial Fix0.00CVE-2021-29603
05/15/20216.36.0Google TensorFlow concatenation.cc integer overflow$5k-$25k$5k-$25kNot DefinedOfficial Fix0.00CVE-2021-29601
05/15/20214.34.1Google TensorFlow conv.cc divide by zero$5k-$25k$0-$5kNot DefinedOfficial Fix0.00CVE-2021-29594
05/15/20216.36.0Google TensorFlow subgraph.c null pointer dereference$5k-$25k$0-$5kNot DefinedOfficial Fix0.00CVE-2021-29592
05/15/20214.34.1Google TensorFlow while.cc While infinite loop$5k-$25k$0-$5kNot DefinedOfficial Fix0.05CVE-2021-29591
05/15/20214.34.1Google TensorFlow pooling.cc divide by zero$5k-$25k$0-$5kNot DefinedOfficial Fix0.04CVE-2021-29586
05/15/20214.34.1Google TensorFlow TFLite padding.h divide by zero$5k-$25k$0-$5kNot DefinedOfficial Fix0.06CVE-2021-29585
05/15/20214.34.1Google TensorFlow fused_batch_norm_op.cc tf.raw_ops.FusedBatchNorm null pointer dereference$5k-$25k$0-$5kNot DefinedOfficial Fix0.06CVE-2021-29583

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