Common Weakness Enumeration

CWE-125

Out-of-bounds Read

The product reads data past the end, or before the beginning, of the intended buffer.

CVE-2022-23467 (GCVE-0-2022-23467)

Vulnerability from cvelistv5 – Published: 2022-12-05 19:22 – Updated: 2025-11-03 19:26
VLAI
Title
Out of Bounds Read in OpenRazer Driver
Summary
OpenRazer is an open source driver and user-space daemon to control Razer device lighting and other features on GNU/Linux. Using a modified USB device an attacker can leak stack addresses of the `razer_attr_read_dpi_stages`, potentially bypassing KASLR. To exploit this vulnerability an attacker would need to access to a users keyboard or mouse or would need to convince a user to use a modified device. The issue has been patched in v3.5.1. Users are advised to upgrade and should be reminded not to plug in unknown USB devices.
Severity
4.4 (Medium)
CVSS:3.1/AV:P/AC:H/PR:L/UI:R/S:C/C:L/I:L/A:L
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
Assigner
References
Impacted products
Vendor Product Version
openrazer openrazer Affected: < 3.5.1
Create a notification for this product.
Show details on NVD website
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CVE-2022-23481 (GCVE-0-2022-23481)

Vulnerability from cvelistv5 – Published: 2022-12-09 17:50 – Updated: 2025-04-23 16:29
VLAI
Title
Out-of-Bound Read in xrdp
Summary
xrdp is an open source project which provides a graphical login to remote machines using Microsoft Remote Desktop Protocol (RDP). xrdp < v0.9.21 contain a Out of Bound Read in xrdp_caps_process_confirm_active() function. There are no known workarounds for this issue. Users are advised to upgrade.
Severity
0 (None)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:N
SSVC
Exploitation: none Automatable: yes Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
Assigner
References
Impacted products
Vendor Product Version
neutrinolabs xrdp Affected: < 0.9.21
Create a notification for this product.
Show details on NVD website
To clipboard 

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CVE-2022-23482 (GCVE-0-2022-23482)

Vulnerability from cvelistv5 – Published: 2022-12-09 17:50 – Updated: 2025-04-23 16:29
VLAI
Title
Out-of-Bound Read in xrdp
Summary
xrdp is an open source project which provides a graphical login to remote machines using Microsoft Remote Desktop Protocol (RDP). xrdp < v0.9.21 contain a Out of Bound Read in xrdp_sec_process_mcs_data_CS_CORE() function. There are no known workarounds for this issue. Users are advised to upgrade.
Severity
0 (None)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:N
SSVC
Exploitation: none Automatable: yes Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
Assigner
References
Impacted products
Vendor Product Version
neutrinolabs xrdp Affected: < 0.9.21
Create a notification for this product.
Show details on NVD website
To clipboard 

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CVE-2022-23483 (GCVE-0-2022-23483)

Vulnerability from cvelistv5 – Published: 2022-12-09 17:50 – Updated: 2025-04-23 16:29
VLAI
Title
Out-of-Bound Read in libxrdp
Summary
xrdp is an open source project which provides a graphical login to remote machines using Microsoft Remote Desktop Protocol (RDP). xrdp < v0.9.21 contain a Out of Bound Read in libxrdp_send_to_channel() function. There are no known workarounds for this issue. Users are advised to upgrade.
Severity
7.5 (High)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N
SSVC
Exploitation: none Automatable: yes Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
Assigner
References
Impacted products
Vendor Product Version
neutrinolabs xrdp Affected: < 0.9.21
Create a notification for this product.
Show details on NVD website
To clipboard 

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CVE-2022-23493 (GCVE-0-2022-23493)

Vulnerability from cvelistv5 – Published: 2022-12-09 17:48 – Updated: 2025-04-23 16:30
VLAI
Title
Out of Bound Read in xrdp
Summary
xrdp is an open source project which provides a graphical login to remote machines using Microsoft Remote Desktop Protocol (RDP). xrdp < v0.9.21 contain a Out of Bound Read in xrdp_mm_trans_process_drdynvc_channel_close() function. There are no known workarounds for this issue. Users are advised to upgrade.
Severity
9.1 (Critical)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:H
SSVC
Exploitation: none Automatable: yes Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
Assigner
References
Impacted products
Vendor Product Version
neutrinolabs xrdp Affected: < 0.9.21
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Show details on NVD website
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CVE-2022-23523 (GCVE-0-2022-23523)

Vulnerability from cvelistv5 – Published: 2022-12-13 07:41 – Updated: 2025-04-18 18:28
VLAI
Title
rust-vmm linux-loader vulnerable to Out-of-bounds Read
Summary
In versions prior to 0.8.1, the linux-loader crate uses the offsets and sizes provided in the ELF headers to determine the offsets to read from. If those offsets point beyond the end of the file this could lead to Virtual Machine Monitors using the `linux-loader` crate entering an infinite loop if the ELF header of the kernel they are loading was modified in a malicious manner. This issue has been addressed in 0.8.1. The issue can be mitigated by ensuring that only trusted kernel images are loaded or by verifying that the headers do not point beyond the end of the file.
Severity
4 (Medium)
CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
  • CWE-125 - Out-of-bounds Read
  • CWE-119 - Improper Restriction of Operations within the Bounds of a Memory Buffer
Assigner
References
Impacted products
Vendor Product Version
rust-vmm linux-loader Affected: < 0.8.1
Create a notification for this product.
Show details on NVD website
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CVE-2022-23560 (GCVE-0-2022-23560)

Vulnerability from cvelistv5 – Published: 2022-02-04 22:32 – Updated: 2025-04-22 18:23
VLAI
Title
Read and Write outside of bounds in TFLite
Summary
Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors. The fix is included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. Users are advised to upgrade as soon as possible.
Severity
8.8 (High)
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
SSVC
Exploitation: poc Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
Assigner
References
Impacted products
Vendor Product Version
tensorflow tensorflow Affected: >= 2.7.0, < 2.7.1
Affected: >= 2.6.0, < 2.6.3
Affected: < 2.5.3
Create a notification for this product.
Show details on NVD website
To clipboard 

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CVE-2022-23574 (GCVE-0-2022-23574)

Vulnerability from cvelistv5 – Published: 2022-02-04 22:32 – Updated: 2025-04-22 18:23
VLAI
Title
Out of bounds read and write in Tensorflow
Summary
Tensorflow is an Open Source Machine Learning Framework. There is a typo in TensorFlow's `SpecializeType` which results in heap OOB read/write. Due to a typo, `arg` is initialized to the `i`th mutable argument in a loop where the loop index is `j`. Hence it is possible to assign to `arg` from outside the vector of arguments. Since this is a mutable proto value, it allows both read and write to outside of bounds data. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.
Severity
8.8 (High)
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
SSVC
Exploitation: poc Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
Assigner
References
Impacted products
Vendor Product Version
tensorflow tensorflow Affected: >= 2.7.0, < 2.7.1
Affected: >= 2.6.0, < 2.6.3
Affected: < 2.5.3
Create a notification for this product.
Show details on NVD website
To clipboard 

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CVE-2022-23592 (GCVE-0-2022-23592)

Vulnerability from cvelistv5 – Published: 2022-02-04 22:32 – Updated: 2025-04-22 18:26
VLAI
Title
Out of bounds read in Tensorflow
Summary
Tensorflow is an Open Source Machine Learning Framework. TensorFlow's type inference can cause a heap out of bounds read as the bounds checking is done in a `DCHECK` (which is a no-op during production). An attacker can control the `input_idx` variable such that `ix` would be larger than the number of values in `node_t.args`. The fix will be included in TensorFlow 2.8.0. This is the only affected version.
Severity
8.1 (High)
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H
SSVC
Exploitation: poc Automatable: no Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
Assigner
References
Impacted products
Vendor Product Version
tensorflow tensorflow Affected: = 2.8.0-rc0
Create a notification for this product.
Show details on NVD website
To clipboard 

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CVE-2022-23594 (GCVE-0-2022-23594)

Vulnerability from cvelistv5 – Published: 2022-02-04 22:32 – Updated: 2025-04-23 19:08
VLAI
Title
Out of bounds read in Tensorflow
Summary
Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming `GraphDef` before converting it to the MLIR-based dialect. If an attacker changes the `SavedModel` format on disk to invalidate these assumptions and the `GraphDef` is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
Severity
8.8 (High)
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator (v2.0.3)
CWE
Assigner
References
Impacted products
Vendor Product Version
tensorflow tensorflow Affected: >= 2.7.0, < 2.8.0
Create a notification for this product.
Show details on NVD website
To clipboard 

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            {
              "lang": "eng",
              "value": "Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming `GraphDef` before converting it to the MLIR-based dialect. If an attacker changes the `SavedModel` format on disk to invalidate these assumptions and the `GraphDef` is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered."
            }
          ]
        },
        "impact": {
          "cvss": {
            "attackComplexity": "LOW",
            "attackVector": "LOCAL",
            "availabilityImpact": "HIGH",
            "baseScore": 8.8,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "LOW",
            "scope": "CHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H",
            "version": "3.1"
          }
        },
        "problemtype": {
          "problemtype_data": [
            {
              "description": [
                {
                  "lang": "eng",
                  "value": "CWE-125: Out-of-bounds Read"
                }
              ]
            }
          ]
        },
        "references": {
          "reference_data": [
            {
              "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9x52-887g-fhc2",
              "refsource": "CONFIRM",
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9x52-887g-fhc2"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/tree/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/core/ir/importexport",
              "refsource": "MISC",
              "url": "https://github.com/tensorflow/tensorflow/tree/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/core/ir/importexport"
            }
          ]
        },
        "source": {
          "advisory": "GHSA-9x52-887g-fhc2",
          "discovery": "UNKNOWN"
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2022-23594",
    "datePublished": "2022-02-04T22:32:11.000Z",
    "dateReserved": "2022-01-19T00:00:00.000Z",
    "dateUpdated": "2025-04-23T19:08:04.448Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
}
Mitigation ID: MIT-5

Phase: Implementation

Strategy: Input Validation

Description:

  • Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
  • When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue."
  • Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
  • To reduce the likelihood of introducing an out-of-bounds read, ensure that you validate and ensure correct calculations for any length argument, buffer size calculation, or offset. Be especially careful of relying on a sentinel (i.e. special character such as NUL) in untrusted inputs.
Mitigation

Phase: Architecture and Design

Strategy: Language Selection

Description:

  • Use a language that provides appropriate memory abstractions.
CAPEC-540: Overread Buffers

An adversary attacks a target by providing input that causes an application to read beyond the boundary of a defined buffer. This typically occurs when a value influencing where to start or stop reading is set to reflect positions outside of the valid memory location of the buffer. This type of attack may result in exposure of sensitive information, a system crash, or arbitrary code execution.

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