Vulnerability-Lookup

FKIE_CVE-2017-1000360

Vulnerability from fkie_nvd - Published: 2017-04-24 16:59 - Updated: 2026-06-17 00:59

Severity

5.3 (Medium) - CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L

Summary

StreamCorruptedException and NullPointerException in OpenDaylight odl-mdsal-xsql. Controller launches exceptions in the console. Component: OpenDaylight odl-mdsal-xsql is vulnerable to this flaw. Version: The tested versions are OpenDaylight 3.3 and 4.0.

References

	URL	Tags
	cve@mitre.org	https://aaltodoc.aalto.fi/bitstream/handle/123456789/21584/master_Bidaj_Andi_2016.pdf	Exploit, Technical Description, Third Party Advisory
	af854a3a-2127-422b-91ae-364da2661108	https://aaltodoc.aalto.fi/bitstream/handle/123456789/21584/master_Bidaj_Andi_2016.pdf	Exploit, Technical Description, Third Party Advisory

Impacted products

	Vendor	Product	Version
	opendaylight	opendaylight	3.3
	opendaylight	opendaylight	4.0

JSON

To clipboard

{
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          "vendor": "n/a",
          "versions": [
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      "source": "cve@mitre.org"
    }
  ],
  "configurations": [
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            },
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  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "StreamCorruptedException and NullPointerException in OpenDaylight odl-mdsal-xsql. Controller launches exceptions in the console. Component: OpenDaylight odl-mdsal-xsql is vulnerable to this flaw. Version: The tested versions are OpenDaylight 3.3 and 4.0."
    },
    {
      "lang": "es",
      "value": "StreamCorruptedException y NullPointerException en OpenDaylight odl-mdsal-xsql. El controlador inicia excepciones en la consola. Componente: OpenDaylight odl-mdsal-xsql es vulnerable a este fallo. Versi\u00f3n: las versiones probadas son OpenDaylight 3.3 y 4.0."
    }
  ],
  "id": "CVE-2017-1000360",
  "lastModified": "2026-06-17T00:59:01.460",
  "metrics": {
    "cvssMetricV2": [
      {
        "acInsufInfo": true,
        "baseSeverity": "MEDIUM",
        "cvssData": {
          "accessComplexity": "LOW",
          "accessVector": "NETWORK",
          "authentication": "NONE",
          "availabilityImpact": "PARTIAL",
          "baseScore": 5.0,
          "confidentialityImpact": "NONE",
          "integrityImpact": "NONE",
          "vectorString": "AV:N/AC:L/Au:N/C:N/I:N/A:P",
          "version": "2.0"
        },
        "exploitabilityScore": 10.0,
        "impactScore": 2.9,
        "obtainAllPrivilege": false,
        "obtainOtherPrivilege": false,
        "obtainUserPrivilege": false,
        "source": "nvd@nist.gov",
        "type": "Primary",
        "userInteractionRequired": false
      }
    ],
    "cvssMetricV30": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "LOW",
          "baseScore": 5.3,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "NONE",
          "integrityImpact": "NONE",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L",
          "version": "3.0"
        },
        "exploitabilityScore": 3.9,
        "impactScore": 1.4,
        "source": "nvd@nist.gov",
        "type": "Primary"
      }
    ]
  },
  "published": "2017-04-24T16:59:00.283",
  "references": [
    {
      "source": "cve@mitre.org",
      "tags": [
        "Exploit",
        "Technical Description",
        "Third Party Advisory"
      ],
      "url": "https://aaltodoc.aalto.fi/bitstream/handle/123456789/21584/master_Bidaj_Andi_2016.pdf"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Exploit",
        "Technical Description",
        "Third Party Advisory"
      ],
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    }
  ],
  "sourceIdentifier": "cve@mitre.org",
  "vulnStatus": "Modified",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-476"
        }
      ],
      "source": "nvd@nist.gov",
      "type": "Primary"
    }
  ]
}

CVE-2017-1000360 (GCVE-0-2017-1000360)

Vulnerability from cvelistv5 – Published: 2017-04-24 16:00 – Updated: 2024-09-17 03:48

Summary

Severity

No CVSS data available.

CWE

Assigner

mitre mitre root

References

1 reference

URL	Tags
https://aaltodoc.aalto.fi/bitstream/handle/123456…	x_refsource_MISC

Show details on NVD website

JSON

To clipboard

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            "url": "https://aaltodoc.aalto.fi/bitstream/handle/123456789/21584/master_Bidaj_Andi_2016.pdf"
          }
        ],
        "title": "CVE Program Container"
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    "cna": {
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          "vendor": "n/a",
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      ],
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        }
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        }
      ],
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.

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.