CVE-2021-29533

{"id": "CVE-2021-29533", "metrics": {"cvssMetricV2": [{"type": "Primary", "source": "nvd@nist.gov", "cvssData": {"version": "2.0", "baseScore": 2.1, "accessVector": "LOCAL", "vectorString": "AV:L/AC:L/Au:N/C:N/I:N/A:P", "authentication": "NONE", "integrityImpact": "NONE", "accessComplexity": "LOW", "availabilityImpact": "PARTIAL", "confidentialityImpact": "NONE"}, "acInsufInfo": false, "impactScore": 2.9, "baseSeverity": "LOW", "obtainAllPrivilege": false, "exploitabilityScore": 3.9, "obtainUserPrivilege": false, "obtainOtherPrivilege": false, "userInteractionRequired": false}], "cvssMetricV31": [{"type": "Secondary", "source": "security-advisories@github.com", "cvssData": {"scope": "UNCHANGED", "version": "3.1", "baseScore": 2.5, "attackVector": "LOCAL", "baseSeverity": "LOW", "vectorString": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L", "integrityImpact": "NONE", "userInteraction": "NONE", "attackComplexity": "HIGH", "availabilityImpact": "LOW", "privilegesRequired": "LOW", "confidentialityImpact": "NONE"}, "impactScore": 1.4, "exploitabilityScore": 1.0}, {"type": "Primary", "source": "nvd@nist.gov", "cvssData": {"scope": "UNCHANGED", "version": "3.1", "baseScore": 5.5, "attackVector": "LOCAL", "baseSeverity": "MEDIUM", "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H", "integrityImpact": "NONE", "userInteraction": "NONE", "attackComplexity": "LOW", "availabilityImpact": "HIGH", "privilegesRequired": "LOW", "confidentialityImpact": "NONE"}, "impactScore": 3.6, "exploitabilityScore": 1.8}]}, "published": "2021-05-14T20:15:12.120", "references": [{"url": "https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87", "tags": ["Patch", "Third Party Advisory"], "source": "security-advisories@github.com"}, {"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-393f-2jr3-cp69", "tags": ["Exploit", "Patch", "Third Party Advisory"], "source": "security-advisories@github.com"}, {"url": "https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87", "tags": ["Patch", "Third Party Advisory"], "source": "af854a3a-2127-422b-91ae-364da2661108"}, {"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-393f-2jr3-cp69", "tags": ["Exploit", "Patch", "Third Party Advisory"], "source": "af854a3a-2127-422b-91ae-364da2661108"}], "vulnStatus": "Modified", "weaknesses": [{"type": "Secondary", "source": "security-advisories@github.com", "description": [{"lang": "en", "value": "CWE-754"}]}], "descriptions": [{"lang": "en", "value": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIRES` to validate user controlled inputs. Whereas `OP_REQUIRES` allows returning an error condition back to the user, the `CHECK_*` macros result in a crash if the condition is false, similar to `assert`. In this case, `height` is 0 from the `images` input. This results in `max_box_row_clamp` being negative and the assertion being falsified, followed by aborting program execution. 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."}, {"lang": "es", "value": "TensorFlow es una plataforma de c\u00f3digo abierto de extremo a extremo para el aprendizaje autom\u00e1tico. Un atacante puede desencadenar una denegaci\u00f3n de servicio por medio de un fallo de \"CHECK\" al pasar una imagen vac\u00eda a \"tf.raw_ops.DrawBoundingBoxes\". Esto es debido a que la implementaci\u00f3n (https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) usa \"CHECIRES_ * para\" comprobar las entradas controladas por el usuario. Mientras que \"OP_REQUIRES\" permite devolver una condici\u00f3n de error al usuario, las macros\" CHECK_ * \"dan como resultado un bloqueo si la condici\u00f3n es falsa, similar a\" assert\". En este caso, \"height\" es 0 de la entrada de \"images\". Esto resulta en que \"max_box_row_clamp\" sea negativo y la aserci\u00f3n se falsifique, seguido de la interrupci\u00f3n de una ejecuci\u00f3n del programa. La correcci\u00f3n ser\u00e1 inclu\u00edda en TensorFlow versi\u00f3n 2.5.0"}], "lastModified": "2024-11-21T06:01:19.577", "configurations": [{"nodes": [{"negate": false, "cpeMatch": [{"criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*", "vulnerable": true, "matchCriteriaId": "323ABCCE-24EB-47CC-87F6-48C101477587", "versionEndExcluding": "2.1.4"}, {"criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*", "vulnerable": true, "matchCriteriaId": "64ABA90C-0649-4BB0-89C9-83C14BBDCC0F", "versionEndExcluding": "2.2.3", "versionStartIncluding": "2.2.0"}, {"criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*", "vulnerable": true, "matchCriteriaId": "0F83E0CF-CBF6-4C24-8683-3E7A5DC95BA9", "versionEndExcluding": "2.3.3", "versionStartIncluding": "2.3.0"}, {"criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*", "vulnerable": true, "matchCriteriaId": "8259531B-A8AC-4F8B-B60F-B69DE4767C03", "versionEndExcluding": "2.4.2", "versionStartIncluding": "2.4.0"}], "operator": "OR"}]}], "sourceIdentifier": "security-advisories@github.com"}