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NVIDIA Megatron-LM EUVDEUVD-2026-98563

| CVE-2026-65104 HIGH
Use of Weak Hash (CWE-328)
2026-10-13 psirt@nvidia.com GHSA-mf54-g87x-8pg4
7.1
CVSS 3.1 · Vendor: nvidia
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Attack ChainAIDerived

Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain

  1. Access
    Obtain low-privilege inference access
  2. Delivery
    Craft colliding cache-key inputs
  3. Exploit
    Exploit weak hash (CWE-328) collision
  4. Execution
    Poison shared inference cache
  5. Impact
    Tamper data / disclose partial cached info

Vulnerability Assessment

Weakness
CWE
OWASP Top 10:2025
A04:2025 - Cryptographic Failures
ASVS 5.0
V11 - Cryptography Irreversible hash functions
Exploitation Conditions

Exploitation requires a Megatron-LM deployment that uses the inference request cache, plus attacker ability to submit inference requests (PR:L - at least low-level/authenticated access to the inference endpoint). The core prerequisite is engineering inputs that collide under the weak hash (CWE-328) used as the cache key; this is reflected by AC:H and is the primary limiting factor. Exploitation is further bounded by the need for the caching mechanism to be active and for the attacker to share the cache with victim requests (the S:C/C:L/I:H impact only materializes against co-tenant/other users' cached data). It does not require user interaction (UI:N) and does not affect availability (A:N).

Impact

This is a moderate-severity issue (CVSS 7.1) that is unlikely to be a top-tier emergency priority. The CVSS vector (AV:N/AC:H/PR:L/UI:N/S:C/C:L/I:H/A:N) indicates a network-reachable flaw but with meaningful friction: high attack complexity (AC:H) and a requirement for at least low-level privileges (PR:L) - i.e. the attacker must already be able to submit inference requests. The scope change (S:C) and high integrity impact (I:H) are what push the score up, reflecting that a successful hash collision in the shared inference cache can tamper with data served to other requests, with only limited (C:L) information disclosure. No EPSS score was provided in the input, and there is no CISA KEV listing and no public exploit identified at time of analysis, so there is no signal of active or widespread exploitation. The combination of CWE-328 (weak hash), AC:H, and the need to deliberately engineer collisions means exploitation is non-trivial and more consistent with a targeted, research-grade attack than opportunistic mass exploitation. Net: a legitimate issue to patch for anyone running Megatron-LM inference with caching, but not a drop-everything priority absent exploitation signals.

Exploit ScenarioDerived

An attacker who can already submit inference requests to a Megatron-LM deployment crafts inputs engineered to collide under the weak hash used to key the inference cache. The collision causes cached results to be mismatched, letting the attacker tamper with data returned to other requests and glean partial information about other cached entries. No public proof-of-concept was identified, and the high attack complexity means the attacker must successfully engineer the collisions.

This Week Schedule remediation this week. Why: HIGH severity (CVSS 7.0-8.9).
No patch listed yet 0.1% exploitation probability (EPSS)

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AnalysisAIv1 – Oct 13, 2026

Hash collisions in the inference request cache of NVIDIA Megatron-LM let an attacker who can already submit inference requests tamper with cached data and obtain partial information about other users' cached requests and responses, yielding a CVSS 7.1 impact (AV:N/AC:H/PR:L/UI:N/S:C/C:L/I:H/A:N). Because the cache key relies on a weak hash (CWE-328), success depends on the attacker engineering colliding inputs against an enabled, shared inference cache, which is why the vector rates complexity as high rather than trivial. No public exploit code has been identified at time of analysis and the CVE is not listed in CISA KEV; this is a moderate-severity issue rather than a top-tier emergency, and the NVIDIA product-security advisory is the authoritative source for affected builds and fixed versions.

RemediationAI

Patch available per vendor advisory: consult the NVIDIA product security bulletin at https://github.com/NVIDIA/product-security/tree/main/2026/5881 and apply the fixed Megatron-LM release listed there, since an exact fixed version is not confirmed from the provided data and should not be assumed. Where immediate upgrading is not possible, the most direct mitigation is to disable the inference request cache for multi-tenant or untrusted-input deployments, accepting the resulting latency and throughput regression from recomputation; alternatively, partition the cache per API key, user, or tenant so a collision can only affect the attacker's own namespace, at the cost of a lower cache hit rate and higher memory footprint. Replacing the weak hash with a collision-resistant, per-deployment secret-keyed digest such as HMAC-SHA-256 reduces feasibility of collisions but requires modifying the caching layer or waiting for the vendor fix. Because exploitation requires PR:L, restricting inference endpoint access to trusted identities and rate-limiting or monitoring request submission raises the bar, and operators should watch for anomalous cache hit patterns or unexpected response content that could indicate collision-driven poisoning.

Affected VersionsENISA

Megatron-LM 0 to 17

Affects your stack

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Technical ContextAI · show details

NVIDIA Megatron-LM is a large-scale transformer training and inference framework built on PyTorch and used in distributed GPU deployments; its inference path can cache prior requests or their results so that repeated or similar inputs avoid recomputation. The root cause is CWE-328, Use of Weak Hash: the cache key is derived with a hash function that lacks collision resistance, so distinct inputs can be driven to the same key. Collisions in such caches are a classic attack pattern and are practical against non-cryptographic digests (for example CRC32/FNV-style checksums), truncated digests, or cryptographic hashes with known collision attacks; the attacker then reuses the victim's cache slot, corrupting the value returned to the victim or learning something about the victim's cached entry. The CVSS 3.1 vector is AV:N (the inference endpoint is network reachable), AC:H (crafting collisions and timing them against an active shared cache is non-trivial), PR:L (at least low-privileged/authenticated access to the inference endpoint is required), UI:N (no victim interaction), S:C (the compromised cache component affects resources outside its own security authority, i.e. co-tenants' data), C:L (only partial disclosure, since a collision reveals limited information about the victim's entry), I:H (cached content can be altered), and A:N (no availability impact).

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Severity by source

Vendor (nvidia) PRIMARY 7.1 HIGH
AV:N/AC:H/PR:L/UI:N/S:C/C:L/I:H/A:N
vuln.today AI 7.1 HIGH

Attacker needs low-privilege inference access (PR:L) and must engineer hash collisions (AC:H); cache poisoning affects other requests (S:C) with high integrity, limited confidentiality, no availability impact.

3.1 AV:N/AC:H/PR:L/UI:N/S:C/C:L/I:H/A:N
4.0 AV:N/AC:H/AT:N/PR:L/UI:N/VC:L/VI:H/VA:N/SC:L/SI:L/SA:N

Primary rating from Vendor (nvidia).

CVSS VectorVendor: nvidia

Attack Vector
Network
Attack Complexity
High
Privileges Required
Low
User Interaction
None
Scope
Changed
Confidentiality
Low
Integrity
High
Availability
None

DescriptionCVE.org

NVIDIA Megatron-LM for all platforms contains a vulnerability in inference, where an attacker could cause hash collisions in the cache used for inference requests. A successful exploit of this vulnerability might lead to data tampering and partial information disclosure.

Executive ViewAI

Why It Matters

NVIDIA Megatron-LM's shared inference cache can allow an authenticated user who can submit inference requests to tamper with cached data and obtain partial information from other users' cached requests and responses. No public exploit identified at time of analysis, the CVE is not listed in CISA KEV, and no vendor-released patch is currently available, so shared or multi-tenant inference deployments carry the greatest exposure. Leadership should prioritize confirming whether the cache is enabled in production and reducing tenant-to-tenant exposure while a fix is pending.

Business Impact: High
Compensating Controls

No vendor-released patch identified at time of analysis. Interim mitigations: disable the shared inference cache if possible; if it must remain enabled, isolate caches per tenant or user and enforce least privilege on inference endpoints; monitor for cache anomalies and cross-request data exposure; restrict inference access to trusted users.

Threat Intelligence Overview

EPSS Score
0.1%
Top 97% exploitable
Prevalence
Medium
popular open-source NVIDIA LLM framework, limited to ML/AI inference workloads
Data Quality
6 sources
Confidence: High
EUVD NVD CVE.org EPSS Vulnrichment GitHub VulDB

How this decision was reached

The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.

SignalValue
Severity HIGH
CISA KEV No
Public POC No
EPSS 0.1%
Patch available No
Active threat No
Deciding rule
HIGH severity (CVSS 7.0-8.9)
What every action level means
Emergency Immediate action required. Criteria: CRITICAL + in CISA KEV (actively exploited) without patch, OR CRITICAL + EPSS > 30% without patch, OR CRITICAL + active threat signals without patch.
Act Now Prioritize within 24-48h. Criteria: in KEV with patch available, OR CRITICAL + public POC, OR HIGH + EPSS > 10%, OR CRITICAL + active threat, OR any CRITICAL severity.
This Week Schedule remediation this week. Criteria: HIGH severity (CVSS 7.0-8.9). Patch within normal change window.
This Month Plan within 30 days. Criteria: MEDIUM severity (CVSS 4.0-6.9), lower exploitation probability. Include in regular patch cycle.
Monitor Track and reassess. Criteria: LOW severity (CVSS 0.1-3.9), minimal exploitation risk. No immediate action needed.

SSVC AssessmentENISA

SSVC Decision
Scheduled
Exploitation
None
System Exposure· derived
Open
Automatable
No
Human Impact
High
Technical Impact
Partial

Priority Score

36
Low Medium High Critical
KEV: 0
EPSS: 0
CVSS: +36
PoC: 0
Weaponized: 0
Threat intel: 0
Gov advisory: 0

Attack Technique

Information disclosure occurs when an application unintentionally exposes sensitive data that aids attackers in reconnaissance or directly compromises security.

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