CVE-2017-1000353 (GCVE-0-2017-1000353)
Vulnerability from cvelistv5 – Published: 2018-01-29 17:00 – Updated: 2025-10-21 23:45Summary
Jenkins versions 2.56 and earlier as well as 2.46.1 LTS and earlier are vulnerable to an unauthenticated remote code execution. An unauthenticated remote code execution vulnerability allowed attackers to transfer a serialized Java `SignedObject` object to the Jenkins CLI, that would be deserialized using a new `ObjectInputStream`, bypassing the existing blacklist-based protection mechanism. We're fixing this issue by adding `SignedObject` to the blacklist. We're also backporting the new HTTP CLI protocol from Jenkins 2.54 to LTS 2.46.2, and deprecating the remoting-based (i.e. Java serialization) CLI protocol, disabling it by default.
Severity
9.8 (Critical)
SSVC
Exploitation: active
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-10-02 03:55 UTC
CWE
- n/a
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://www.exploit-db.com/exploits/41965/ | exploitx_refsource_EXPLOIT-DB |
| http://www.securityfocus.com/bid/98056 | vdb-entryx_refsource_BID |
| https://www.oracle.com/security-alerts/cpuapr2022.html | x_refsource_MISC |
| https://jenkins.io/security/advisory/2017-04-26/ | x_refsource_CONFIRM |
| http://packetstormsecurity.com/files/159266/Jenki… | x_refsource_MISC |
| https://www.cisa.gov/known-exploited-vulnerabilit… | government-resource |
Date Public
2017-04-26 00:00
CISA
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: c6aa3e81-d776-44b7-837d-1f8d27bc8024
Exploited: Yes
Timestamps
First Seen: 2025-10-02
Asserted: 2025-10-02
Scope
Notes: KEV entry: Jenkins Remote Code Execution Vulnerability | Affected: Jenkins / Jenkins | Description: Jenkins contains a remote code execution vulnerability. This vulnerability that could allowed attackers to transfer a serialized Java SignedObject object to the remoting-based Jenkins CLI, that would be deserialized using a new ObjectInputStream, bypassing the existing blocklist-based protection mechanism. | Required action: Apply mitigations per vendor instructions, follow applicable BOD 22-01 guidance for cloud services, or discontinue use of the product if mitigations are unavailable. | Due date: 2025-10-23 | Known ransomware campaign use (KEV): Unknown | Notes (KEV): https://www.jenkins.io/security/advisory/2017-04-26/ ; https://nvd.nist.gov/vuln/detail/CVE-2017-1000353
Evidence
Type: Vendor Report
Signal: Successful Exploitation
Confidence: 80%
Source: cisa-kev
Details
| Cwes | |
|---|---|
| Feed | CISA Known Exploited Vulnerabilities Catalog |
| Product | Jenkins |
| Due Date | 2025-10-23 |
| Date Added | 2025-10-02 |
| Vendorproject | Jenkins |
| Vulnerabilityname | Jenkins Remote Code Execution Vulnerability |
| Knownransomwarecampaignuse | Unknown |
References
Created: 2026-02-02 13:24 UTC
| Updated: 2026-02-06 07:53 UTC
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 12f80f4c-1513-4651-a109-62ef330c6554
Exploited: Yes
Timestamps
First Seen: 2026-06-01
Asserted: 2026-06-01
Scope
Notes: KEVIntel entry: Jenkins versions 2.56 and earlier as well as 2.46.1 LTS and earlier are vulnerable to an unauthenticated remote code execution. An unauthenticated... | Affected: Jenkins / Jenkins | CVSS: 9.8 (CRITICAL) | EPSS: 0.99686 | Used in malware: unknown | Not yet in CISA KEV: False
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: kevintel
Details
| Feed | KEVIntel (kevintel.com) |
|---|---|
| Title | Jenkins versions 2.56 and earlier as well as 2.46.1 LTS and earlier are vulnerable to an unauthenticated remote code execution. An unauthenticated... |
| Vendor | Jenkins |
| Product | Jenkins |
| Added Date | 2026-06-01T10:41:37.417Z |
| Cvss Score | 9.8 |
| Epss Score | 0.99686 |
| Cvss Severity | CRITICAL |
| Epss Percentile | 0.99949 |
| Used In Malware | unknown |
| Ahead Of Cisa Kev |
|
| Not Yet In Cisa Kev | False |
References
Created: 2026-06-19 12:42 UTC
| Updated: 2026-06-19 12:42 UTC
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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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