ATLAS Landscape
AML.T0001
Search Open AI Vulnerability Analysis
Much like the [Search Open Technical Databases](/techniques/AML.T0000), there is often ample research available on the vulnerabilities of common AI models. Once a target has been identified, an adversary will likely try to identify any pre-existing work that has been done for this class of models. This will include not only reading academic papers that may identify the particulars of a successful attack, but also identifying pre-existing implementations of those attacks. The adversary may obtain [Adversarial AI Attack Implementations](/techniques/AML.T0016.000) or develop their own [Adversarial AI Attacks](/techniques/AML.T0017.000) if necessary.
11 CVEs mapped
View on MITRE ATLAS →
| Severity | CVE | Headline | Package | CVSS |
|---|---|---|---|---|
| CRITICAL | CVE-2020-15202 | TensorFlow: Shard API int truncation enables memory corruption | tensorflow | 9.0 |
| HIGH | CVE-2021-37656 | TensorFlow: null ptr deref in RaggedTensorToSparse op | tensorflow | 7.8 |
| HIGH | CVE-2021-29583 | TensorFlow: heap overflow in FusedBatchNorm risks RCE | tensorflow | 7.8 |
| HIGH | CVE-2021-29512 | TensorFlow: heap buffer overflow in RaggedBincount op | tensorflow | 7.8 |
| HIGH | CVE-2022-35963 | TensorFlow: DoS via FractionalAvgPoolGrad overflow | tensorflow | 7.5 |
| HIGH | CVE-2021-37641 | TensorFlow: RaggedGather OOB read - heap leak + DoS | tensorflow | 7.1 |
| MEDIUM | CVE-2018-21233 | TensorFlow: integer overflow leaks process memory via BMP | tensorflow | 6.5 |
| MEDIUM | CVE-2025-46150 | PyTorch: torch.compile silent output inconsistency | pytorch | 5.3 |
| MEDIUM | CVE-2025-46149 | PyTorch: reachable assertion in nn.Fold with inductor | pytorch | 5.3 |
| MEDIUM | CVE-2025-46148 | PyTorch: PairwiseDistance silent miscalculation, integrity risk | pytorch | 5.3 |
| LOW | CVE-2026-7845 | Langchain-Chatchat: weak image hash allows integrity bypass | langchain-chatchat | 2.6 |
AI Threat Alert