Common Weakness Enumeration

CWE-131

Allowed

Incorrect Calculation of Buffer Size

Abstraction: Base · Status: Draft

API response cwe.mitre.org

The product does not correctly calculate the size to be used when allocating a buffer, which could lead to a buffer overflow.

290 vulnerabilities reference this CWE, most recent first.

GHSA-762H-VPVW-3RCX

Vulnerability from github – Published: 2022-11-21 20:40 – Updated: 2022-11-21 20:40
VLAI
Summary
Overflow in `FusedResizeAndPadConv2D`
Details

Impact

When tf.raw_ops.FusedResizeAndPadConv2D is given a large tensor shape, it overflows.

import tensorflow as tf

mode = "REFLECT"
strides = [1, 1, 1, 1]
padding = "SAME"
resize_align_corners = False
input = tf.constant(147, shape=[3,3,1,1], dtype=tf.float16)
size = tf.constant([1879048192,1879048192], shape=[2], dtype=tf.int32)
paddings = tf.constant([3,4], shape=[2], dtype=tf.int32)
filter = tf.constant(123, shape=[1,3,4,1], dtype=tf.float16)
tf.raw_ops.FusedResizeAndPadConv2D(input=input, size=size, paddings=paddings, filter=filter, mode=mode, strides=strides, padding=padding, resize_align_corners=resize_align_corners)

Patches

We have patched the issue in GitHub commit d66e1d568275e6a2947de97dca7a102a211e01ce.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Neophytos Christou from the Secure Systems Lab (SSL) at Brown University.

Severity
4.8 (Medium)
CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H
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{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.7.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.8.0"
            },
            {
              "fixed": "2.8.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.9.0"
            },
            {
              "fixed": "2.9.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.7.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.7.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.8.0"
            },
            {
              "fixed": "2.8.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.8.0"
            },
            {
              "fixed": "2.8.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.9.0"
            },
            {
              "fixed": "2.9.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.9.0"
            },
            {
              "fixed": "2.9.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2022-41885"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-131"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2022-11-21T20:40:42Z",
    "nvd_published_at": "2022-11-18T22:15:00Z",
    "severity": "MODERATE"
  },
  "details": "### Impact\nWhen [`tf.raw_ops.FusedResizeAndPadConv2D`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/conv_ops_fused_image_transform.cc) is given a large tensor shape, it overflows.\n```python\nimport tensorflow as tf\n\nmode = \"REFLECT\"\nstrides = [1, 1, 1, 1]\npadding = \"SAME\"\nresize_align_corners = False\ninput = tf.constant(147, shape=[3,3,1,1], dtype=tf.float16)\nsize = tf.constant([1879048192,1879048192], shape=[2], dtype=tf.int32)\npaddings = tf.constant([3,4], shape=[2], dtype=tf.int32)\nfilter = tf.constant(123, shape=[1,3,4,1], dtype=tf.float16)\ntf.raw_ops.FusedResizeAndPadConv2D(input=input, size=size, paddings=paddings, filter=filter, mode=mode, strides=strides, padding=padding, resize_align_corners=resize_align_corners)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [d66e1d568275e6a2947de97dca7a102a211e01ce](https://github.com/tensorflow/tensorflow/commit/d66e1d568275e6a2947de97dca7a102a211e01ce).\n\nThe fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.\n\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n\n\n### Attribution\nThis vulnerability has been reported by Neophytos Christou from the Secure Systems Lab (SSL) at Brown University.\n",
  "id": "GHSA-762h-vpvw-3rcx",
  "modified": "2022-11-21T20:40:42Z",
  "published": "2022-11-21T20:40:42Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-762h-vpvw-3rcx"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-41885"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/d66e1d568275e6a2947de97dca7a102a211e01ce"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/conv_ops_fused_image_transform.cc"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "Overflow in `FusedResizeAndPadConv2D`"
}

GHSA-76J2-HF4J-XJHQ

Vulnerability from github – Published: 2022-08-23 00:00 – Updated: 2023-05-03 15:30
VLAI
Details

An out-of-bounds memory access flaw was found in the Linux kernel Intel’s iSMT SMBus host controller driver in the way a user triggers the I2C_SMBUS_BLOCK_DATA (with the ioctl I2C_SMBUS) with malicious input data. This flaw allows a local user to crash the system.

Severity
5.5 (Medium)
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Show details on source website
To clipboard 

{
  "affected": [],
  "aliases": [
    "CVE-2022-2873"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-131"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2022-08-22T15:15:00Z",
    "severity": "MODERATE"
  },
  "details": "An out-of-bounds memory access flaw was found in the Linux kernel Intel\u2019s iSMT SMBus host controller driver in the way a user triggers the I2C_SMBUS_BLOCK_DATA (with the ioctl I2C_SMBUS) with malicious input data. This flaw allows a local user to crash the system.",
  "id": "GHSA-76j2-hf4j-xjhq",
  "modified": "2023-05-03T15:30:16Z",
  "published": "2022-08-23T00:00:17Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-2873"
    },
    {
      "type": "WEB",
      "url": "https://lists.debian.org/debian-lts-announce/2023/03/msg00000.html"
    },
    {
      "type": "WEB",
      "url": "https://lists.debian.org/debian-lts-announce/2023/05/msg00006.html"
    },
    {
      "type": "WEB",
      "url": "https://lore.kernel.org/lkml/20220729093451.551672-1-zheyuma97@gmail.com/T"
    },
    {
      "type": "WEB",
      "url": "https://security.netapp.com/advisory/ntap-20230120-0001"
    },
    {
      "type": "WEB",
      "url": "https://www.debian.org/security/2023/dsa-5324"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-77RV-4WJP-CFHF

Vulnerability from github – Published: 2022-11-09 12:00 – Updated: 2022-11-09 19:02
VLAI
Details

In typec, there is a possible out of bounds write due to an incorrect calculation of buffer size. This could lead to local escalation of privilege, for an attacker who has physical access to the device, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07262454; Issue ID: ALPS07262454.

Severity
6.8 (Medium)
CVSS:3.1/AV:P/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
Show details on source website
To clipboard 

{
  "affected": [],
  "aliases": [
    "CVE-2022-32618"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-131"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2022-11-08T21:15:00Z",
    "severity": "MODERATE"
  },
  "details": "In typec, there is a possible out of bounds write due to an incorrect calculation of buffer size. This could lead to local escalation of privilege, for an attacker who has physical access to the device, with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07262454; Issue ID: ALPS07262454.",
  "id": "GHSA-77rv-4wjp-cfhf",
  "modified": "2022-11-09T19:02:23Z",
  "published": "2022-11-09T12:00:22Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-32618"
    },
    {
      "type": "WEB",
      "url": "https://corp.mediatek.com/product-security-bulletin/November-2022"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:P/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-78JW-WW3G-9WP7

Vulnerability from github – Published: 2026-06-17 18:35 – Updated: 2026-08-25 15:32
VLAI
Details

NGINX Plus and NGINX Open Source have a vulnerability in the ngx_http_proxy_v2_module and ngx_http_grpc_module modules. This vulnerability exists when the proxy_http_version to 2 or grpc_pass directives are used to proxy HTTP/2 traffic, the ignore_invalid_headers directive is set to off, and the large_client_header_buffers directive size is larger than 2 megabytes. A remote, unauthenticated attacker, along with conditions beyond their control, could send large headers while creating an upstream request. This may cause a heap-based buffer overflow in the NGINX worker process leading to a restart. Additionally, attackers can execute code on systems with Address Space Layout Randomization (ASLR) disabled or when the attacker can bypass ASLR.

Note: Software versions which have reached End of Technical Support (EoTS) are not evaluated.

Severity
8.1 (High)
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H
9.2 (Critical)
CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
Show details on source website
To clipboard 

{
  "affected": [],
  "aliases": [
    "CVE-2026-42055"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-122",
      "CWE-131",
      "CWE-787"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-06-17T15:16:50Z",
    "severity": "CRITICAL"
  },
  "details": "NGINX Plus and NGINX Open Source have a vulnerability in the ngx_http_proxy_v2_module\u00a0and ngx_http_grpc_module\u00a0modules. This vulnerability exists when the proxy_http_version to 2\u00a0or grpc_pass\u00a0directives are used to proxy HTTP/2 traffic, the ignore_invalid_headers\u00a0directive is set to off, and the large_client_header_buffers\u00a0directive size is larger than 2 megabytes. A remote, unauthenticated attacker, along with conditions beyond their control, could send large headers while creating an upstream request. This may cause a heap-based buffer overflow in the NGINX worker process leading to a restart. Additionally, attackers can execute code on systems with Address Space Layout Randomization (ASLR) disabled or when the attacker can bypass ASLR. \n\n\nNote: Software versions which have reached End of Technical Support (EoTS) are not evaluated.",
  "id": "GHSA-78jw-ww3g-9wp7",
  "modified": "2026-08-25T15:32:42Z",
  "published": "2026-06-17T18:35:56Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-42055"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2026:27197"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2026:36331"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2026:36364"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2026:36618"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2026:36639"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2026:38847"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2026:44481"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2026:46836"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/errata/RHSA-2026:58981"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/security/cve/CVE-2026-42055"
    },
    {
      "type": "WEB",
      "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2489866"
    },
    {
      "type": "WEB",
      "url": "https://my.f5.com/manage/s/article/K000161584"
    },
    {
      "type": "WEB",
      "url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-42055.json"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
      "type": "CVSS_V4"
    }
  ]
}

GHSA-7MV6-R5F6-W598

Vulnerability from github – Published: 2022-05-01 01:49 – Updated: 2024-02-02 03:30
VLAI
Details

Multiple stack-based buffer overflows in libcURL and cURL 7.12.1, and possibly other versions, allow remote malicious web servers to execute arbitrary code via base64 encoded replies that exceed the intended buffer lengths when decoded, which is not properly handled by (1) the Curl_input_ntlm function in http_ntlm.c during NTLM authentication or (2) the Curl_krb_kauth and krb4_auth functions in krb4.c during Kerberos authentication.

Severity
8.8 (High)
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Show details on source website
To clipboard 

{
  "affected": [],
  "aliases": [
    "CVE-2005-0490"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-131"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2005-05-02T04:00:00Z",
    "severity": "MODERATE"
  },
  "details": "Multiple stack-based buffer overflows in libcURL and cURL 7.12.1, and possibly other versions, allow remote malicious web servers to execute arbitrary code via base64 encoded replies that exceed the intended buffer lengths when decoded, which is not properly handled by (1) the Curl_input_ntlm function in http_ntlm.c during NTLM authentication or (2) the Curl_krb_kauth and krb4_auth functions in krb4.c during Kerberos authentication.",
  "id": "GHSA-7mv6-r5f6-w598",
  "modified": "2024-02-02T03:30:30Z",
  "published": "2022-05-01T01:49:47Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2005-0490"
    },
    {
      "type": "WEB",
      "url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/19423"
    },
    {
      "type": "WEB",
      "url": "https://oval.cisecurity.org/repository/search/definition/oval%3Aorg.mitre.oval%3Adef%3A10273"
    },
    {
      "type": "WEB",
      "url": "http://distro.conectiva.com.br/atualizacoes/?id=a\u0026anuncio=000940"
    },
    {
      "type": "WEB",
      "url": "http://marc.info/?l=full-disclosure\u0026m=110959085507755\u0026w=2"
    },
    {
      "type": "WEB",
      "url": "http://www.gentoo.org/security/en/glsa/glsa-200503-20.xml"
    },
    {
      "type": "WEB",
      "url": "http://www.idefense.com/application/poi/display?id=202\u0026type=vulnerabilities"
    },
    {
      "type": "WEB",
      "url": "http://www.idefense.com/application/poi/display?id=203\u0026type=vulnerabilities"
    },
    {
      "type": "WEB",
      "url": "http://www.mandriva.com/security/advisories?name=MDKSA-2005:048"
    },
    {
      "type": "WEB",
      "url": "http://www.novell.com/linux/security/advisories/2005_11_curl.html"
    },
    {
      "type": "WEB",
      "url": "http://www.redhat.com/support/errata/RHSA-2005-340.html"
    },
    {
      "type": "WEB",
      "url": "http://www.securityfocus.com/bid/12615"
    },
    {
      "type": "WEB",
      "url": "http://www.securityfocus.com/bid/12616"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-7QM6-RR75-XC68

Vulnerability from github – Published: 2022-05-24 19:11 – Updated: 2022-12-26 03:30
VLAI
Details

An issue was discovered in Mbed TLS before 2.25.0 (and before 2.16.9 LTS and before 2.7.18 LTS). The calculations performed by mbedtls_mpi_exp_mod are not limited; thus, supplying overly large parameters could lead to denial of service when generating Diffie-Hellman key pairs.

Severity
7.5 (High)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Show details on source website
To clipboard 

{
  "affected": [],
  "aliases": [
    "CVE-2020-36475"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-131"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2021-08-23T02:15:00Z",
    "severity": "HIGH"
  },
  "details": "An issue was discovered in Mbed TLS before 2.25.0 (and before 2.16.9 LTS and before 2.7.18 LTS). The calculations performed by mbedtls_mpi_exp_mod are not limited; thus, supplying overly large parameters could lead to denial of service when generating Diffie-Hellman key pairs.",
  "id": "GHSA-7qm6-rr75-xc68",
  "modified": "2022-12-26T03:30:21Z",
  "published": "2022-05-24T19:11:59Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2020-36475"
    },
    {
      "type": "WEB",
      "url": "https://cert-portal.siemens.com/productcert/pdf/ssa-756638.pdf"
    },
    {
      "type": "WEB",
      "url": "https://github.com/ARMmbed/mbedtls/releases/tag/v2.16.9"
    },
    {
      "type": "WEB",
      "url": "https://github.com/ARMmbed/mbedtls/releases/tag/v2.25.0"
    },
    {
      "type": "WEB",
      "url": "https://github.com/ARMmbed/mbedtls/releases/tag/v2.7.18"
    },
    {
      "type": "WEB",
      "url": "https://lists.debian.org/debian-lts-announce/2021/11/msg00021.html"
    },
    {
      "type": "WEB",
      "url": "https://lists.debian.org/debian-lts-announce/2022/12/msg00036.html"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-7R8W-VVVJ-23GM

Vulnerability from github – Published: 2022-12-05 15:30 – Updated: 2022-12-06 21:30
VLAI
Details

In throttling, there is a possible out of bounds write due to an incorrect calculation of buffer size. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07405923; Issue ID: ALPS07405923.

Severity
6.7 (Medium)
CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H
Show details on source website
To clipboard 

{
  "affected": [],
  "aliases": [
    "CVE-2022-32624"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-131",
      "CWE-787"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2022-12-05T15:15:00Z",
    "severity": "MODERATE"
  },
  "details": "In throttling, there is a possible out of bounds write due to an incorrect calculation of buffer size. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07405923; Issue ID: ALPS07405923.",
  "id": "GHSA-7r8w-vvvj-23gm",
  "modified": "2022-12-06T21:30:46Z",
  "published": "2022-12-05T15:30:27Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-32624"
    },
    {
      "type": "WEB",
      "url": "https://corp.mediatek.com/product-security-bulletin/December-2022"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-83VM-P52W-F9PW

Vulnerability from github – Published: 2026-05-06 21:45 – Updated: 2026-06-08 19:52
VLAI
Summary
vLLM: extract_hidden_states speculative decoding crashes server on any request with penalty parameters
Details

Summary

The extract_hidden_states speculative decoding proposer in vLLM returns a tensor with an incorrect shape after the first decode step, causing a RuntimeError that crashes the EngineCore process. The crash is triggered when any request in the batch uses sampling penalty parameters (repetition_penalty, frequency_penalty, or presence_penalty).

A single request with a penalty parameter (e.g., "repetition_penalty": 1.1) is sufficient to crash the server. The crash is deterministic and immediate — no concurrency, race condition, or special workload is required.

Details

In vLLM v0.17.0, the extract_hidden_states proposer's propose() method returned sampled_token_ids.unsqueeze(-1), producing a tensor of shape (batch_size, 1).

In PR #37013 (first released in v0.18.0), the KV connector interface was refactored out of propose(). The return type changed from tuple[Tensor, KVConnectorOutput | None] to Tensor, and the .unsqueeze(-1) call was removed along with the KV connector output:

# Before (v0.17.0):
return sampled_token_ids.unsqueeze(-1), kv_connector_output  # shape (batch_size, 1)

# After (v0.18.0+):
return sampled_token_ids  # shape (batch_size, 2) after first decode step

The refactor missed that sampled_token_ids changed semantics between the first and subsequent decode steps. After the first decode step, the rejection sampler allocates its output as (batch_size, max_spec_len + 1). With num_speculative_tokens=1, this produces shape (batch_size, 2) instead of the expected (batch_size, 1), causing a broadcast shape mismatch during penalty application.

Impact

Any vLLM deployment between v0.18.0 and v0.19.1 (inclusive) configured with extract_hidden_states speculative decoding is affected. A single API request containing any penalty parameter immediately and permanently crashes the EngineCore process, resulting in complete loss of service availability.

Patches

Fixed in PR #38610, first included in vLLM v0.20.0. The fix slices the return value to sampled_token_ids[:, :1], ensuring the correct (batch_size, 1) shape regardless of the rejection sampler's output dimensions.

Workarounds

  • Upgrade to vLLM v0.20.0 or later.
  • If upgrading is not possible, avoid using extract_hidden_states as the speculative decoding method on affected versions.
  • Alternatively, reject or strip penalty parameters (repetition_penalty, frequency_penalty, presence_penalty) from incoming requests at an API gateway before they reach vLLM.
Severity
6.5 (Medium)
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Show details on source website
To clipboard 

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "vllm"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.18.0"
            },
            {
              "fixed": "0.20.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-44223"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-131",
      "CWE-704"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-05-06T21:45:51Z",
    "nvd_published_at": "2026-05-12T20:16:43Z",
    "severity": "MODERATE"
  },
  "details": "### Summary\n\nThe `extract_hidden_states` speculative decoding proposer in vLLM returns a tensor with an incorrect shape after the first decode step, causing a `RuntimeError` that crashes the EngineCore process. The crash is triggered when any request in the batch uses sampling penalty parameters (`repetition_penalty`, `frequency_penalty`, or `presence_penalty`).\n\nA single request with a penalty parameter (e.g., `\"repetition_penalty\": 1.1`) is sufficient to crash the server. The crash is deterministic and immediate \u2014 no concurrency, race condition, or special workload is required.\n\n### Details\n\nIn vLLM v0.17.0, the `extract_hidden_states` proposer\u0027s `propose()` method returned `sampled_token_ids.unsqueeze(-1)`, producing a tensor of shape `(batch_size, 1)`.\n\nIn [PR #37013](https://github.com/vllm-project/vllm/pull/37013) (first released in v0.18.0), the KV connector interface was refactored out of `propose()`. The return type changed from `tuple[Tensor, KVConnectorOutput | None]` to `Tensor`, and the `.unsqueeze(-1)` call was removed along with the KV connector output:\n\n```python\n# Before (v0.17.0):\nreturn sampled_token_ids.unsqueeze(-1), kv_connector_output  # shape (batch_size, 1)\n\n# After (v0.18.0+):\nreturn sampled_token_ids  # shape (batch_size, 2) after first decode step\n```\n\nThe refactor missed that `sampled_token_ids` changed semantics between the first and subsequent decode steps. After the first decode step, the rejection sampler allocates its output as `(batch_size, max_spec_len + 1)`. With `num_speculative_tokens=1`, this produces shape `(batch_size, 2)` instead of the expected `(batch_size, 1)`, causing a broadcast shape mismatch during penalty application.\n\n### Impact\n\nAny vLLM deployment between v0.18.0 and v0.19.1 (inclusive) configured with `extract_hidden_states` speculative decoding is affected. A single API request containing any penalty parameter immediately and permanently crashes the EngineCore process, resulting in complete loss of service availability.\n\n### Patches\n\nFixed in [PR #38610](https://github.com/vllm-project/vllm/pull/38610), first included in vLLM v0.20.0. The fix slices the return value to `sampled_token_ids[:, :1]`, ensuring the correct `(batch_size, 1)` shape regardless of the rejection sampler\u0027s output dimensions.\n\n### Workarounds\n\n- Upgrade to vLLM v0.20.0 or later.\n- If upgrading is not possible, avoid using `extract_hidden_states` as the speculative decoding method on affected versions.\n- Alternatively, reject or strip penalty parameters (`repetition_penalty`, `frequency_penalty`, `presence_penalty`) from incoming requests at an API gateway before they reach vLLM.",
  "id": "GHSA-83vm-p52w-f9pw",
  "modified": "2026-06-08T19:52:35Z",
  "published": "2026-05-06T21:45:51Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-83vm-p52w-f9pw"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-44223"
    },
    {
      "type": "WEB",
      "url": "https://github.com/vllm-project/vllm/pull/38610"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2026-145.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/vllm-project/vllm"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "vLLM: extract_hidden_states speculative decoding crashes server on any request with penalty parameters"
}

GHSA-8C89-2VWR-CHCQ

Vulnerability from github – Published: 2021-05-21 14:22 – Updated: 2024-10-30 23:25
VLAI
Summary
Heap buffer overflow in `QuantizedResizeBilinear`
Details

Impact

An attacker can cause a heap buffer overflow in QuantizedResizeBilinear by passing in invalid thresholds for the quantization:

import tensorflow as tf

images = tf.constant([], shape=[0], dtype=tf.qint32)
size = tf.constant([], shape=[0], dtype=tf.int32) 
min = tf.constant([], dtype=tf.float32)
max = tf.constant([], dtype=tf.float32)

tf.raw_ops.QuantizedResizeBilinear(images=images, size=size, min=min, max=max, align_corners=False, half_pixel_centers=False)

This is because the implementation assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly:

const float in_min = context->input(2).flat<float>()(0);
const float in_max = context->input(3).flat<float>()(0);

However, if any of these tensors is empty, then .flat<T>() is an empty buffer and accessing the element at position 0 results in overflow.

Patches

We have patched the issue in GitHub commit f6c40f0c6cbf00d46c7717a26419f2062f2f8694.

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.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.

Severity
2.5 (Low)
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
2.0 (Low)
CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N
Show details on source website
To clipboard 

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
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              "introduced": "0"
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              "fixed": "2.1.4"
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          "type": "ECOSYSTEM"
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        "name": "tensorflow"
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        "name": "tensorflow-gpu"
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        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
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            {
              "introduced": "2.3.0"
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            }
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        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29537"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-131",
      "CWE-787"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T22:35:23Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nAn attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization:\n\n```python\nimport tensorflow as tf\n\nimages = tf.constant([], shape=[0], dtype=tf.qint32)\nsize = tf.constant([], shape=[0], dtype=tf.int32) \nmin = tf.constant([], dtype=tf.float32)\nmax = tf.constant([], dtype=tf.float32)\n\ntf.raw_ops.QuantizedResizeBilinear(images=images, size=size, min=min, max=max, align_corners=False, half_pixel_centers=False)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly:\n\n```cc\nconst float in_min = context-\u003einput(2).flat\u003cfloat\u003e()(0);\nconst float in_max = context-\u003einput(3).flat\u003cfloat\u003e()(0);\n```\n\nHowever, if any of these tensors is empty, then `.flat\u003cT\u003e()` is an empty buffer and accessing the element at position 0 results in overflow.\n\n### Patches \nWe have patched the issue in GitHub commit [f6c40f0c6cbf00d46c7717a26419f2062f2f8694](https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694).\n\nThe 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.\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n\n### Attribution\nThis vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.",
  "id": "GHSA-8c89-2vwr-chcq",
  "modified": "2024-10-30T23:25:19Z",
  "published": "2021-05-21T14:22:35Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8c89-2vwr-chcq"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29537"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694"
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    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-465.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-663.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-174.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Heap buffer overflow in `QuantizedResizeBilinear`"
}

GHSA-8FVV-46HW-VPG3

Vulnerability from github – Published: 2022-11-21 20:41 – Updated: 2022-11-21 20:41
VLAI
Summary
Overflow in `tf.keras.losses.poisson`
Details

Impact

tf.keras.losses.poisson receives a y_pred and y_true that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment.

import numpy as np
import tensorflow as tf

true_value = tf.reshape(shape=[1, 2500000000], tensor = tf.zeros(dtype=tf.bool, shape=[50000, 50000]))
pred_value = np.array([[[-2]], [[8]]], dtype = np.float64)

tf.keras.losses.poisson(y_true=true_value,y_pred=pred_value)

Patches

We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Pattarakrit Rattankul.

Severity
4.8 (Medium)
CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H
Show details on source website
To clipboard 

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        "name": "tensorflow"
      },
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  ],
  "aliases": [
    "CVE-2022-41887"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-131"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2022-11-21T20:41:35Z",
    "nvd_published_at": "2022-11-18T22:15:00Z",
    "severity": "MODERATE"
  },
  "details": "### Impact\n[`tf.keras.losses.poisson`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/losses.py) receives a `y_pred` and `y_true` that are passed through `functor::mul` in [`BinaryOp`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/cwise_ops_common.h). If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment.\n```python\nimport numpy as np\nimport tensorflow as tf\n\ntrue_value = tf.reshape(shape=[1, 2500000000], tensor = tf.zeros(dtype=tf.bool, shape=[50000, 50000]))\npred_value = np.array([[[-2]], [[8]]], dtype = np.float64)\n\ntf.keras.losses.poisson(y_true=true_value,y_pred=pred_value)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c](https://github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c).\n\nThe fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.\n\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n\n\n### Attribution\nThis vulnerability has been reported by Pattarakrit Rattankul.\n",
  "id": "GHSA-8fvv-46hw-vpg3",
  "modified": "2022-11-21T20:41:35Z",
  "published": "2022-11-21T20:41:35Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fvv-46hw-vpg3"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-41887"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/cwise_ops_common.h"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/losses.py"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "Overflow in `tf.keras.losses.poisson`"
}
Mitigation
Implementation

When allocating a buffer for the purpose of transforming, converting, or encoding an input, allocate enough memory to handle the largest possible encoding. For example, in a routine that converts "&" characters to "&amp;" for HTML entity encoding, the output buffer needs to be at least 5 times as large as the input buffer.

Mitigation MIT-36
Implementation
  • Understand the programming language's underlying representation and how it interacts with numeric calculation (CWE-681). Pay close attention to byte size discrepancies, precision, signed/unsigned distinctions, truncation, conversion and casting between types, "not-a-number" calculations, and how the language handles numbers that are too large or too small for its underlying representation. [REF-7]
  • Also be careful to account for 32-bit, 64-bit, and other potential differences that may affect the numeric representation.
Mitigation MIT-8
Implementation

Strategy: Input Validation

Perform input validation on any numeric input by ensuring that it is within the expected range. Enforce that the input meets both the minimum and maximum requirements for the expected range.

Mitigation MIT-15
Architecture and Design

For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.

Mitigation
Implementation

When processing structured incoming data containing a size field followed by raw data, identify and resolve any inconsistencies between the size field and the actual size of the data (CWE-130).

Mitigation
Implementation

When allocating memory that uses sentinels to mark the end of a data structure - such as NUL bytes in strings - make sure you also include the sentinel in your calculation of the total amount of memory that must be allocated.

Mitigation MIT-13
Implementation

Replace unbounded copy functions with analogous functions that support length arguments, such as strcpy with strncpy. Create these if they are not available.

Mitigation
Implementation

Use sizeof() on the appropriate data type to avoid CWE-467.

Mitigation
Implementation

Use the appropriate type for the desired action. For example, in C/C++, only use unsigned types for values that could never be negative, such as height, width, or other numbers related to quantity. This will simplify validation and will reduce surprises related to unexpected casting.

Mitigation MIT-4
Architecture and Design

Strategy: Libraries or Frameworks

  • Use a vetted library or framework that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid [REF-1482].
  • Use libraries or frameworks that make it easier to handle numbers without unexpected consequences, or buffer allocation routines that automatically track buffer size.
  • Examples include safe integer handling packages such as SafeInt (C++) or IntegerLib (C or C++). [REF-106]
Mitigation MIT-10
Operation Build and Compilation

Strategy: Environment Hardening

  • Use automatic buffer overflow detection mechanisms that are offered by certain compilers or compiler extensions. Examples include: the Microsoft Visual Studio /GS flag, Fedora/Red Hat FORTIFY_SOURCE GCC flag, StackGuard, and ProPolice, which provide various mechanisms including canary-based detection and range/index checking.
  • D3-SFCV (Stack Frame Canary Validation) from D3FEND [REF-1334] discusses canary-based detection in detail.
Mitigation MIT-11
Operation Build and Compilation

Strategy: Environment Hardening

  • Run or compile the software using features or extensions that randomly arrange the positions of a program's executable and libraries in memory. Because this makes the addresses unpredictable, it can prevent an attacker from reliably jumping to exploitable code.
  • Examples include Address Space Layout Randomization (ASLR) [REF-58] [REF-60] and Position-Independent Executables (PIE) [REF-64]. Imported modules may be similarly realigned if their default memory addresses conflict with other modules, in a process known as "rebasing" (for Windows) and "prelinking" (for Linux) [REF-1332] using randomly generated addresses. ASLR for libraries cannot be used in conjunction with prelink since it would require relocating the libraries at run-time, defeating the whole purpose of prelinking.
  • For more information on these techniques see D3-SAOR (Segment Address Offset Randomization) from D3FEND [REF-1335].
Mitigation MIT-12
Operation

Strategy: Environment Hardening

  • Use a CPU and operating system that offers Data Execution Protection (using hardware NX or XD bits) or the equivalent techniques that simulate this feature in software, such as PaX [REF-60] [REF-61]. These techniques ensure that any instruction executed is exclusively at a memory address that is part of the code segment.
  • For more information on these techniques see D3-PSEP (Process Segment Execution Prevention) from D3FEND [REF-1336].
Mitigation MIT-26
Implementation

Strategy: Compilation or Build Hardening

Examine compiler warnings closely and eliminate problems with potential security implications, such as signed / unsigned mismatch in memory operations, or use of uninitialized variables. Even if the weakness is rarely exploitable, a single failure may lead to the compromise of the entire system.

Mitigation MIT-17
Architecture and Design Operation

Strategy: Environment Hardening

Run your code using the lowest privileges that are required to accomplish the necessary tasks [REF-76]. If possible, create isolated accounts with limited privileges that are only used for a single task. That way, a successful attack will not immediately give the attacker access to the rest of the software or its environment. For example, database applications rarely need to run as the database administrator, especially in day-to-day operations.

Mitigation MIT-22
Architecture and Design Operation

Strategy: Sandbox or Jail

  • Run the code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which files can be accessed in a particular directory or which commands can be executed by the software.
  • OS-level examples include the Unix chroot jail, AppArmor, and SELinux. In general, managed code may provide some protection. For example, java.io.FilePermission in the Java SecurityManager allows the software to specify restrictions on file operations.
  • This may not be a feasible solution, and it only limits the impact to the operating system; the rest of the application may still be subject to compromise.
  • Be careful to avoid CWE-243 and other weaknesses related to jails.
CAPEC-100: Overflow Buffers

Buffer Overflow attacks target improper or missing bounds checking on buffer operations, typically triggered by input injected by an adversary. As a consequence, an adversary is able to write past the boundaries of allocated buffer regions in memory, causing a program crash or potentially redirection of execution as per the adversaries' choice.

CAPEC-47: Buffer Overflow via Parameter Expansion

In this attack, the target software is given input that the adversary knows will be modified and expanded in size during processing. This attack relies on the target software failing to anticipate that the expanded data may exceed some internal limit, thereby creating a buffer overflow.

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