CVE-2017-1000486 (GCVE-0-2017-1000486)
Vulnerability from cvelistv5 – Published: 2018-01-03 20:00 – Updated: 2025-10-21 23:45Summary
Primetek Primefaces 5.x is vulnerable to a weak encryption flaw resulting in remote code execution
Severity
9.8 (Critical)
SSVC
Exploitation: active
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-02-07 14:04 UTC
CWE
- n/a
- CWE-326 - Inadequate Encryption Strength
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://github.com/primefaces/primefaces/issues/1152 | x_refsource_CONFIRM |
| https://cryptosense.com/weak-encryption-flaw-in-p… | x_refsource_MISC |
| http://blog.mindedsecurity.com/2016/02/rce-in-ora… | x_refsource_MISC |
| https://www.exploit-db.com/exploits/43733/ | exploitx_refsource_EXPLOIT-DB |
| https://www.cisa.gov/known-exploited-vulnerabilit… | government-resource |
Date Public
2016-02-15 00:00
CISA
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 897e222e-4585-4dc8-aef7-40dab3682878
Exploited: Yes
Timestamps
First Seen: 2022-01-10
Asserted: 2022-01-10
Scope
Notes: KEV entry: Primetek Primefaces Remote Code Execution Vulnerability | Affected: Primetek / Primefaces Application | Description: Primetek Primefaces is vulnerable to a weak encryption flaw resulting in remote code execution | Required action: Apply updates per vendor instructions. | Due date: 2022-07-10 | Known ransomware campaign use (KEV): Unknown | Notes (KEV): https://nvd.nist.gov/vuln/detail/CVE-2017-1000486
Evidence
Type: Vendor Report
Signal: Successful Exploitation
Confidence: 80%
Source: cisa-kev
Details
| Cwes | CWE-326 |
|---|---|
| Feed | CISA Known Exploited Vulnerabilities Catalog |
| Product | Primefaces Application |
| Due Date | 2022-07-10 |
| Date Added | 2022-01-10 |
| Vendorproject | Primetek |
| Vulnerabilityname | Primetek Primefaces Remote Code Execution Vulnerability |
| Knownransomwarecampaignuse | Unknown |
References
Created: 2026-02-02 12:28 UTC
| Updated: 2026-02-06 07:17 UTC
Shadowserver
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 01f9f907-f180-444b-b18f-cece6e670b8c
Exploited: Yes
Characteristics
Severity:
98.0
Timestamps
First Seen: 2023-10-14
Asserted: 2023-10-14
Last Seen: 2026-07-20
Scope
Asset Exposure: ['internet-facing']
Notes: Affected: PrimeFaces / PrimeTek | Class: other-software | Severity: Critical (CVSS 9.8) | IoT: no | In CISA KEV: yes | Honeypot connections on 2026-07-20: 5
Evidence
Type: Honeypot
Signal: In The Wild Attempts
Confidence: 70%
Source: shadowserver
Details
| 1D | 2 |
|---|---|
| Iot | no |
| Feed | Shadowserver Foundation honeypot/exploited-vulnerabilities |
| Type | http-scan |
| Class | other-software |
| 7D Avg | 1 |
| Vendor | PrimeFaces |
| 30D Avg | 2 |
| 90D Avg | 2 |
| Product | PrimeTek |
| Cisa Kev | yes |
| Connections | 5 |
| Observation Date | 2026-07-20 |
| Vulnerability Class | CVSS |
| Vulnerability Score | 9.8 |
| Vulnerability Severity | Critical |
References
Created: 2026-06-30 09:23 UTC
| Updated: 2026-07-22 01:00 UTC
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 964c9404-7cff-416b-aee3-90f1bed340d5
Exploited: Yes
Timestamps
First Seen: 2022-01-10
Asserted: 2022-01-10
Scope
Notes: KEVIntel entry: Primetek Primefaces 5.x is vulnerable to a weak encryption flaw resulting in remote code execution | Affected: Primetek / Primefaces | CVSS: 9.8 (CRITICAL) | EPSS: 0.94104 | 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 | Primetek Primefaces 5.x is vulnerable to a weak encryption flaw resulting in remote code execution |
| Vendor | Primetek |
| Product | Primefaces |
| Added Date | 2022-01-10T00:00:00.000Z |
| Cvss Score | 9.8 |
| Epss Score | 0.94104 |
| Cvss Severity | CRITICAL |
| Epss Percentile | 0.99837 |
| Used In Malware | unknown |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | False |
References
Created: 2026-06-23 14:04 UTC
| Updated: 2026-07-10 06:14 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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