Vulnerability-Lookup

GHSA-5277-MWH4-Q4CC

Vulnerability from github – Published: 2026-08-20 21:31 – Updated: 2026-08-20 21:31

Details

hank-ai/darknet sizes a convolutional layer's weight and output heap buffers by multiplying configuration fields taken from a .cfg file in unchecked 32-bit int arithmetic. In src-lib/convolutional_layer.cpp, l.nweights is computed as (c / groups) * n * size * size and l.outputs as l.out_h * l.out_w * l.out_c, and both feed xcalloc directly. A .cfg whose true dimension product exceeds INT_MAX wraps to a small or zero value, so the allocation is undersized; for example width and height of 256 with filters of 65536 gives 2^32, which wraps to 0. forward_convolutional_layer then re-derives the GEMM dimensions with a different operand order, computing k as l.sizel.sizel.c / l.groups where the allocation divided before multiplying, and reads and writes through the undersized buffer. Loading the crafted .cfg for inference or training is sufficient and no valid .weights file is required. The reported proof of concept observed a heap buffer overflow read in gemm_nn_fast under AddressSanitizer and glibc allocator metadata corruption in a release build of the same input, indicating an out-of-bounds write.

Severity

7.8 (High)


                  
                    CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

8.5 (High)


                  
                    CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

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{
  "affected": [],
  "aliases": [
    "CVE-2026-72852"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-190"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-08-20T19:17:00Z",
    "severity": "HIGH"
  },
  "details": "hank-ai/darknet sizes a convolutional layer\u0027s weight and output heap buffers by multiplying configuration fields taken from a .cfg file in unchecked 32-bit int arithmetic. In src-lib/convolutional_layer.cpp, l.nweights is computed as (c / groups) * n * size * size and l.outputs as l.out_h * l.out_w * l.out_c, and both feed xcalloc directly. A .cfg whose true dimension product exceeds INT_MAX wraps to a small or zero value, so the allocation is undersized; for example width and height of 256 with filters of 65536 gives 2^32, which wraps to 0. forward_convolutional_layer then re-derives the GEMM dimensions with a different operand order, computing k as l.size*l.size*l.c / l.groups where the allocation divided before multiplying, and reads and writes through the undersized buffer. Loading the crafted .cfg for inference or training is sufficient and no valid .weights file is required. The reported proof of concept observed a heap buffer overflow read in gemm_nn_fast under AddressSanitizer and glibc allocator metadata corruption in a release build of the same input, indicating an out-of-bounds write.",
  "id": "GHSA-5277-mwh4-q4cc",
  "modified": "2026-08-20T21:31:28Z",
  "published": "2026-08-20T21:31:28Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-72852"
    },
    {
      "type": "WEB",
      "url": "https://github.com/hank-ai/darknet/issues/148"
    },
    {
      "type": "WEB",
      "url": "https://github.com/hank-ai/darknet"
    },
    {
      "type": "WEB",
      "url": "https://github.com/hank-ai/darknet/blob/v6.0/src-lib/convolutional_layer.cpp#L1457"
    },
    {
      "type": "WEB",
      "url": "https://github.com/hank-ai/darknet/blob/v6.0/src-lib/convolutional_layer.cpp#L764"
    },
    {
      "type": "WEB",
      "url": "https://github.com/hank-ai/darknet/blob/v6.0/src-lib/convolutional_layer.cpp#L811"
    },
    {
      "type": "WEB",
      "url": "https://www.vulncheck.com/advisories/darknet-integer-overflow-in-convolutional-layer-buffer-sizing-leads-to-heap-buffer-overflow"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:P/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"
    }
  ]
}

CVE-2026-72852 (GCVE-0-2026-72852)

Vulnerability from cvelistv5 – Published: 2026-08-20 18:19 – Updated: 2026-08-25 15:18

Title

darknet Integer Overflow in Convolutional Layer Buffer Sizing Leads to Heap Buffer Overflow

Summary

hank-ai/darknet sizes a convolutional layer's weight and output heap buffers by multiplying configuration fields taken from a .cfg file in unchecked 32-bit int arithmetic. In src-lib/convolutional_layer.cpp, l.nweights is computed as (c / groups) * n * size * size and l.outputs as l.out_h * l.out_w * l.out_c, and both feed xcalloc directly. A .cfg whose true dimension product exceeds INT_MAX wraps to a small or zero value, so the allocation is undersized; for example width and height of 256 with filters of 65536 gives 2^32, which wraps to 0. forward_convolutional_layer then re-derives the GEMM dimensions with a different operand order, computing k as l.size*l.size*l.c / l.groups where the allocation divided before multiplying, and reads and writes through the undersized buffer. Loading the crafted .cfg for inference or training is sufficient and no valid .weights file is required. The reported proof of concept observed a heap buffer overflow read in gemm_nn_fast under AddressSanitizer and glibc allocator metadata corruption in a release build of the same input, indicating an out-of-bounds write.

Severity

8.5 (High)


                        
                          CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

7.8 (High)


                        
                          CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

SSVC

Exploitation: poc Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-08-25 15:17 UTC

CWE

CWE-190 - Integer Overflow or Wraparound
CWE-787 - Out-of-bounds Write

Assigner

VulnCheck mitre root

References

6 references

URL	Tags
https://github.com/hank-ai/darknet/issues/148	issue-tracking
https://github.com/hank-ai/darknet/blob/v6.0/src-…	technical-description
https://github.com/hank-ai/darknet/blob/v6.0/src-…	technical-description
https://github.com/hank-ai/darknet/blob/v6.0/src-…	technical-description
https://github.com/hank-ai/darknet	product
https://www.vulncheck.com/advisories/darknet-inte…	third-party-advisory

Impacted products

1 product

Vendor	Product	Version
hank-ai	darknet	Affected: 0 , ≤ 6.0 (custom)

Date Public

2026-08-13 00:00

Credits

mtholmquist

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Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Detection rules are retrieved from Rulezet.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.

Action not permitted

GHSA-5277-MWH4-Q4CC

CVE-2026-72852 (GCVE-0-2026-72852)

Tags

Sightings

Nomenclature