CVE-2023-0297 (GCVE-0-2023-0297)
Vulnerability from cvelistv5 – Published: 2023-01-14 00:00 – Updated: 2025-04-07 18:41Title
Code Injection in pyload/pyload
Summary
Code Injection in GitHub repository pyload/pyload prior to 0.5.0b3.dev31.
Severity
9.8 (Critical)
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-04-07 18:41 UTC
CWE
- CWE-94 - Improper Control of Generation of Code
Assigner
References
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| pyload | pyload/pyload |
Affected:
unspecified , < 0.5.0b3.dev31
(custom)
|
guessed |
Shadowserver
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: c950e1e7-3635-4981-a945-4046a692eda7
Exploited: Yes
Characteristics
Severity:
98.0
Timestamps
First Seen: 2023-11-12
Asserted: 2023-11-12
Last Seen: 2023-11-12
Scope
Asset Exposure: ['internet-facing']
Notes: Affected: pyLoad / pyLoad | Class: other-software | Severity: Critical (CVSS 9.8) | IoT: no | In CISA KEV: no | Honeypot connections on 2023-05-24: 228
Evidence
Type: Honeypot
Signal: In The Wild Attempts
Confidence: 70%
Source: shadowserver
Details
| 1D | 1 |
|---|---|
| Iot | no |
| Feed | Shadowserver Foundation honeypot/exploited-vulnerabilities |
| Type | http-scan |
| Class | other-software |
| 7D Avg | 0 |
| Vendor | pyLoad |
| 30D Avg | 0 |
| 90D Avg | 0 |
| Product | pyLoad |
| Cisa Kev | no |
| Connections | 228 |
| Observation Date | 2023-05-24 |
| Vulnerability Class | CVSS |
| Vulnerability Score | 9.8 |
| Vulnerability Severity | Critical |
References
Created: 2026-06-30 10:22 UTC
| Updated: 2026-06-30 15:22 UTC
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: 97e0616a-22e1-486c-bf0a-8b71ebf2e83d
Exploited: Yes
Timestamps
First Seen: 2023-01-14
Asserted: 2023-01-14
Scope
Notes: KEVIntel entry: Code Injection in pyload/pyload | Affected: pyload / pyload/pyload | CVSS: 9.8 (CRITICAL) | Used in malware: unknown | Not yet in CISA KEV: True
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: kevintel
Details
| Feed | KEVIntel (kevintel.com) |
|---|---|
| Title | Code Injection in pyload/pyload |
| Vendor | pyload |
| Product | pyload/pyload |
| Added Date | 2023-01-14T00:00:00.000Z |
| Cvss Score | 9.8 |
| Epss Score | None |
| Cvss Severity | CRITICAL |
| Epss Percentile | None |
| Used In Malware | unknown |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-06-23 14:06 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 |
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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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