FKIE_CVE-2004-1070
Vulnerability from fkie_nvd - Published: 2005-01-10 05:00 - Updated: 2026-06-16 22:06
Severity
Summary
The load_elf_binary function in the binfmt_elf loader (binfmt_elf.c) in Linux kernel 2.4.x up to 2.4.27, and 2.6.x up to 2.6.8, does not properly check return values from calls to the kernel_read function, which may allow local users to modify sensitive memory in a setuid program and execute arbitrary code.
References
Impacted products
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],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "The load_elf_binary function in the binfmt_elf loader (binfmt_elf.c) in Linux kernel 2.4.x up to 2.4.27, and 2.6.x up to 2.6.8, does not properly check return values from calls to the kernel_read function, which may allow local users to modify sensitive memory in a setuid program and execute arbitrary code."
},
{
"lang": "es",
"value": "El cargador binfmt_elf (binfmt_elf.c) del kernel de Linux 2.4x a 2.4.27, y 2.6.x a 2.6.8 no verifica adecuadamente los valores de retorno de llamadas a la funci\u00f3n kernel_read, lo que puede permitir a usuarios locales modificar informaci\u00f3n sensible en un programa setuid y ejecutar c\u00f3digo de su elecci\u00f3n."
}
],
"id": "CVE-2004-1070",
"lastModified": "2026-06-16T22:06:57.363",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "HIGH",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "LOCAL",
"authentication": "NONE",
"availabilityImpact": "COMPLETE",
"baseScore": 7.2,
"confidentialityImpact": "COMPLETE",
"integrityImpact": "COMPLETE",
"vectorString": "AV:L/AC:L/Au:N/C:C/I:C/A:C",
"version": "2.0"
},
"exploitabilityScore": 3.9,
"impactScore": 10.0,
"obtainAllPrivilege": true,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": false
}
]
},
"published": "2005-01-10T05:00:00.000",
"references": [
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],
"sourceIdentifier": "cve@mitre.org",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "NVD-CWE-Other"
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],
"source": "nvd@nist.gov",
"type": "Primary"
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]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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
| 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.
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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
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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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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