TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a CHECK fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is nullptr. Hence, when calling png::WriteImageToBuffer(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., image.flat<T>().data()) is NULL. This then triggers the CHECK_NOTNULL in the first line of png::WriteImageToBuffer(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since image is null, this results in abort being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. 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.