TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in tf.raw_ops.FractionalAvgPool. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of input_size[i] and pooling_ratio_[i] (via the value.shape() and pooling_ratio arguments). If the value in input_size[i] is smaller than the pooling_ratio_[i], then the floor operation results in output_size[i] being 0. The DCHECK_GT line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to GeneratePoolingSequence(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since output_length can be 0, this results in runtime crashing. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
| Software | From | Fixed in |
|---|---|---|
| google / tensorflow | 2.4.0 | 2.4.2 |
| google / tensorflow | 2.3.0 | 2.3.3 |
| google / tensorflow | 2.2.0 | 2.2.3 |
| google / tensorflow | - | 2.1.4 |
tensorflow
|
- | 2.1.4 |
tensorflow
|
2.2.0 | 2.2.3 |
tensorflow
|
2.3.0 | 2.3.3 |
tensorflow
|
2.4.0 | 2.4.2 |
tensorflow-cpu
|
- | 2.1.4 |
tensorflow-cpu
|
2.2.0 | 2.2.3 |
tensorflow-cpu
|
2.3.0 | 2.3.3 |
tensorflow-cpu
|
2.4.0 | 2.4.2 |
tensorflow-gpu
|
- | 2.1.4 |
tensorflow-gpu
|
2.2.0 | 2.2.3 |
tensorflow-gpu
|
2.3.0 | 2.3.3 |
tensorflow-gpu
|
2.4.0 | 2.4.2 |
A security vulnerability is a weakness in software, hardware, or configuration that can be exploited to compromise confidentiality, integrity, or availability. Many vulnerabilities are tracked as CVEs (Common Vulnerabilities and Exposures), which provide a standardized identifier so teams can coordinate patching, mitigation, and risk assessment across tools and vendors.
CVSS (Common Vulnerability Scoring System) estimates technical severity, but it doesn't automatically equal business risk. Prioritize using context like internet exposure, affected asset criticality, known exploitation (proof-of-concept or in-the-wild), and whether compensating controls exist. A "Medium" CVSS on an exposed, production system can be more urgent than a "Critical" on an isolated, non-production host.
A vulnerability is the underlying weakness. An exploit is the method or code used to take advantage of it. A zero-day is a vulnerability that is unknown to the vendor or has no publicly available fix when attackers begin using it. In practice, risk increases sharply when exploitation becomes reliable or widespread.
Recurring findings usually come from incomplete Asset Discovery, inconsistent patch management, inherited images, and configuration drift. In modern environments, you also need to watch the software supply chain: dependencies, containers, build pipelines, and third-party services can reintroduce the same weakness even after you patch a single host. Unknown or unmanaged assets (often called Shadow IT) are a common reason the same issues resurface.
Use a simple, repeatable triage model: focus first on externally exposed assets, high-value systems (identity, VPN, email, production), vulnerabilities with known exploits, and issues that enable remote code execution or privilege escalation. Then enforce patch SLAs and track progress using consistent metrics so remediation is steady, not reactive.
SynScan combines attack surface monitoring and continuous security auditing to keep your inventory current, flag high-impact vulnerabilities early, and help you turn raw findings into a practical remediation plan.