🎯 CVE-2025-25183 🧪 PoC 🇪🇺 EUVD
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CVE-2025-25183 | vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintended behavior. Prefix caching makes use of Python's built-in hash() function. As of Python 3.12, the behavior of hash(None) has changed to be a predictable constant value. This makes it more feasible that someone could try ex

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintended behavior. Prefix caching makes use of Python's built-in hash() function. As of Python 3.12, the behavior of hash(None) has changed to be a predictable constant value. This makes it more feasible that someone could try exploit hash collisions. The impact of a collision would be using cache that was generated using different content. Given knowledge of prompts in use and predictable hashing behavior, someone could intentionally populate the cache using a prompt known to collide with another prompt in use. This issue has been addressed in version 0.7.2 and all users are advised to upgrade. There are no known workarounds for this vulnerability.

Klassifikation & Betroffenheit:
vllm-project vllm < 0.7.2vllm vllm *
Improper Control of Generation of Code ('Code Injection') 🎯 Medium

The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.

🛡️ Empfohlene Mitigation: Refactor your program so that you do not have to dynamically generate code.
Vollständige Definition bei MITRE ➔
🧩 Ähnliche Schwachstellen (Hersteller/Klasse/Score-Band):
🩹 Patch verfügbar (OSV):
🩹 432117cd1f59c76d97da2eaff55a7d758301dbc7 (Commit) 🩹 0408efc6d0c17fba17b2be38d0d0f02e96d2bf9d (Commit)
📚 Referenzen & Quellen:
Ausnutzungs-Zeitleiste:
CVSS-Vektor-Analyse: 2.6
AV · Angriffsvektor Netzwerk
AC · Komplexität Hoch
PR · Privilegien Gering
UI · Interaktion Erforderlich
S · Scope Unverändert
C · Vertraulichkeit Keine
I · Integrität Gering
A · Verfügbarkeit Keine
CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:L/A:N
Veröffentlicht:07.02.2025
Aktualisiert:17.06.2026 09:00
Assigner (CNA):GitHub_M
EUVD-ID:EUVD-2025-4074
Quellen: 🇪🇺 EUVD-Datenbank (ENISA) + 🇺🇸 NVD-Anreicherung · 24-h-Cache
CWE-94: Code Injection ✓ Offizieller Patch / Advisory verfügbar
💡 Gegenmaßnahme: Sicherheits-Update des Herstellers zeitnah einspielen und Netzwerksegmentierung prüfen.
🔴 Live Security Advisory & EPSS Exploit Radar

Zero-Day & Vulnerability Intelligence Hub

Echtzeit-Tracking mit EPSS Exploit-Wahrscheinlichkeiten, Angriffsvektor-Decodern und KI-Patch-Anleitungen.

367k+ 🇪🇺 EUVD-Datenbank
0 🔴 Critical im Radar
0 ⚠️ CISA KEV
0 🔓 Aktiv ausgenutzt
1 🧪 PoC verfügbar
📊 Historien-Charts — Criticals-Trend · Vendors · EPSS-Verteilung
🔴 Criticals pro Monat (12 M) 2025-09: 80 2025-10: 317 2025-11: 257 2025-12: 426 2026-01: 431 2026-02: 417 2026-03: 649 2026-04: 574 2026-05: 683 2026-06: 941 2026-07: 1327 2026-08: 1828 2026-09: 1025 8.955 Criticals gesamt
🏢 Top-Vendor-Veröffentlichungen (6 M) Adobe Apple Google Linux Microsoft Oracle Corporation
● Adobe ● Apple ● Google ● Linux ● Microsoft ● Oracle
📈 EPSS-Verteilung (Messungen)
Tier2026-09-022026-09-20
≥90 %0489
≥50 %01477
≥10 %00
<10 %3000
Datenquellen & Methodik: Primärquelle ist die EUVD der ENISA (laufender Datenbank-Sync, alle 15 Minuten), abgeglichen mit dem CISA-KEV-Katalog und der NVD — Detail-Dossiers reichern fehlende Felder live per NVD an — mit Fallback auf CIRCL vulnerability-lookup (EU/Non-Profit, aggregiert CVE-, GitHub- und OSV-Advisories). Der CISA-KEV-Katalog (Known Exploited Vulnerabilities, ~1.700 aktiv ausgenutzte Schwachstellen) wird bei jedem Sync vollständig neu geladen und kreuzreferenziert — filterbar über die KEV-Pille. CVSS 3.1 wird nach Ampel-Logik aus Verteidigersicht dekodiert; EPSS bezeichnet die 30-Tage-Exploit-Wahrscheinlichkeit (FIRST.org).
🇪🇺 ENISA EUVD 🇺🇸 NVD ⚠️ CISA KEV ⚡ EPSS
Ökosystem & Hersteller Bedrohungs-Matrix:
Schweregrad & Status:
Hersteller (Datenbank-weit, 96.590 Einträge):
Quelle:
Schwachstellen-Kategorie (CWE):
🔍
EPSS 0.2%
CVE-2025-25183 🌐 Netzwerk (Remote) 🔑 Geringe Nutzerrechte nötig
vllm-project

CVE-2025-25183 | vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintended behavior. Prefix caching makes use of Python's built-in hash() function. As of Python 3.12, the behavior of hash(None) has changed to be a predictable constant value. This makes it more feasible that someone could try ex

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintend

CWE-94: Code Injection ✓ Offizieller Patch / Advisory verfügbar
💡 Gegenmaßnahme: Sicherheits-Update des Herstellers zeitnah einspielen und Netzwerksegmentierung prüfen.