FKIE_CVE-2007-3763
Vulnerability from fkie_nvd - Published: 2007-07-18 17:30 - Updated: 2026-06-16 22:42
Severity
Summary
The IAX2 channel driver (chan_iax2) in Asterisk before 1.2.22 and 1.4.x before 1.4.8, Business Edition before B.2.2.1, AsteriskNOW before beta7, Appliance Developer Kit before 0.5.0, and s800i before 1.0.2 allows remote attackers to cause a denial of service (crash) via a crafted (1) LAGRQ or (2) LAGRP frame that contains information elements of IAX frames, which results in a NULL pointer dereference when Asterisk does not properly set an associated variable.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| asterisk | asterisk | 1.0 | |
| asterisk | asterisk | 1.0.6 | |
| asterisk | asterisk | 1.0.7 | |
| asterisk | asterisk | 1.0.8 | |
| asterisk | asterisk | 1.0.9 | |
| asterisk | asterisk | 1.0.10 | |
| asterisk | asterisk | 1.0.11 | |
| asterisk | asterisk | 1.0.12 | |
| asterisk | asterisk | 1.2.0_beta1 | |
| asterisk | asterisk | 1.2.0_beta2 | |
| asterisk | asterisk | 1.2.5 | |
| asterisk | asterisk | 1.2.6 | |
| asterisk | asterisk | 1.2.7 | |
| asterisk | asterisk | 1.2.8 | |
| asterisk | asterisk | 1.2.9 | |
| asterisk | asterisk | 1.2.10 | |
| asterisk | asterisk | 1.2.11 | |
| asterisk | asterisk | 1.2.12 | |
| asterisk | asterisk | 1.2.13 | |
| asterisk | asterisk | 1.2.14 | |
| asterisk | asterisk | 1.2.15 | |
| asterisk | asterisk | 1.2.16 | |
| asterisk | asterisk | 1.2.17 | |
| asterisk | asterisk | 1.4.1 | |
| asterisk | asterisk | 1.4.2 | |
| asterisk | asterisk | 1.4.4_2007-04-27 | |
| asterisk | asterisk | 1.4_beta | |
| asterisk | asterisk | a | |
| asterisk | asterisk | b.1.3.2 | |
| asterisk | asterisk | b.1.3.3 | |
| asterisk | asterisk | b.2.2.0 | |
| asterisk | asterisk_appliance_developer_kit | * | |
| asterisk | asterisknow | beta_5 | |
| asterisk | asterisknow | beta_6 | |
| asterisk | s800i_appliance | 1.0 | |
| asterisk | s800i_appliance | 1.0.1 |
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"vendor": "n/a",
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"source": "cve@mitre.org"
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"id": "CVE-2007-3763",
"lastModified": "2026-06-16T22:42:41.233",
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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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