VulnerabilityPublished 25 Nov 2025

CVE-2025-33191: NVIDIA DGX Spark · Improper Input Validation

Affects NVIDIA DGX SparkSource CVE Database V5

Not known to be exploited · CVSS 5.7 Medium.

Data as of 26 Oct 2026, 15:59 UTC

CVSS 3.1 base score

5.7Medium

How severe the flaw is on the 0–10 CVSS scale.

EPSS: 0.1% chance of exploitation in 30 days – higher than 6% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:C/C:L/I:N/A:L

Attack Vector: Local
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Scope: Changed
Confidentiality: Low
Integrity: None
Availability: Low

Bold values are the worst case: easiest to exploit or largest impact.

Attack Vector: Local: The vulnerable component is not bound to the network stack and the attacker’s path is via read/write/execute capabilities. Either: the attacker exploits the vulnerability by accessing the target system locally (e.g., keyboard, console), or remotely (e.g., SSH); or the attacker relies on User Interaction by another person to perform actions required to exploit the vulnerability (e.g., tricking a legitimate user into opening a malicious document).
Attack Complexity: Low: Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success against the vulnerable component.
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files to carry out an attack.
User Interaction: None: The vulnerable system can be exploited without any interaction from any user.
Scope: Changed: An exploited vulnerability can affect resources beyond the security scope managed by the security authority of the vulnerable component. In this case, the vulnerable component and the impacted component are different and managed by different security authorities.
Confidentiality: Low: There is some loss of confidentiality. Access to some restricted information is obtained, but the attacker does not have control over what information is obtained, or the amount or kind of loss is limited. The information disclosure does not cause a direct, serious loss to the impacted component.
Integrity: None: There is no loss of integrity within the impacted component.
Availability: Low: Performance is reduced or there are interruptions in resource availability. Even if repeated exploitation of the vulnerability is possible, the attacker does not have the ability to completely deny service to legitimate users. The resources in the impacted component are either partially available all of the time, or fully available only some of the time, but overall there is no direct, serious consequence to the impacted component.

Definitions: FIRST CVSS v3.1 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 25 Nov 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.

Disclosed: The CVE record is public on cve.org. Its publication is t = 0.
Detectable: A public nuclei template can find exposed instances, for defenders and attackers alike.
Weaponised: Public exploit code exists in Metasploit or Exploit-DB, or CISA SSVC or the CVSS exploit maturity reports a public proof of concept.
Exploited in the wild: CISA KEV lists it, CISA SSVC rates exploitation active, or a source reports attacks.
Observed by OffSeq Mirage: OffSeq Mirage honeypot sensors recorded attack traffic for this CVE, or found exposed hosts without attack traffic.

Moments are spaced evenly, not to scale; a dashed line marks a gap of six months or more. Deltas count from t = 0. Sources that give only a date (CISA KEV, Exploit-DB, or a time of exactly 00:00Z) read “same day” rather than hours. Metasploit modules carry the flaw’s disclosure date, not their release date, and nuclei templates carry no date, so both appear above but not on the timeline.

What to do

AI-suggested

Restrict physical and local access to NVIDIA DGX Spark systems to trusted personnel only, implementing strict access controls and monitoring.
Employ network segmentation and isolation for DGX Spark environments to limit exposure to potentially malicious internal actors.
Monitor system logs and firmware behavior for anomalies indicative of attempted exploitation or instability.
Develop incident response plans specifically addressing potential DoS scenarios on critical AI infrastructure.
Coordinate with NVIDIA for timely notification and deployment of firmware updates or patches once available.
Consider deploying host-based intrusion detection systems (HIDS) to detect unusual local activities targeting firmware components.
Conduct regular security audits and firmware integrity checks to ensure system components have not been tampered with.
Educate administrators and operators on the risks associated with local firmware vulnerabilities and the importance of secure operational practices.

Description

NVIDIA DGX Spark GB10 contains a vulnerability in OSROOT firmware, where an attacker could cause an invalid memory read. A successful exploit of this vulnerability might lead to denial of service.

Impact

For European organizations, the primary impact of CVE-2025-33191 is operational disruption due to denial of service on NVIDIA DGX Spark systems. These systems are often deployed in AI research, scientific computing, and data centers, where uptime and reliability are critical. A successful exploit could halt AI model training or inference tasks, delaying projects and causing financial and reputational damage. Confidentiality risks are minimal, but availability loss could affect service delivery and SLAs. Organizations with large-scale HPC or AI infrastructure, especially in sectors like automotive, aerospace, finance, and academia, may experience significant operational setbacks. The requirement for local access limits remote exploitation, but insider threats or compromised internal networks could still pose risks. The lack of current patches means organizations must rely on compensating controls until updates are released. Given the strategic importance of AI and HPC in Europe’s digital economy, disruptions could have broader impacts on innovation and competitiveness.

CWE-20Improper Input Validation (opens in a new tab)
Class weaknessLikelihood of exploit: HighCWE Top 25 (2025): #18OWASP Top 10 A05:2025: Injection (opens in a new tab)
The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.
MITRE discourages mapping vulnerabilities to this broad entry; the root cause is usually a more specific weakness.
Architecture and Design · Attack Surface Reduction
Consider using language-theoretic security (LangSec) techniques that characterize inputs using a formal language and build "recognizers" for that language. This effectively requires parsing to be a distinct layer that effectively enforces a boundary between raw input and internal data representations, instead of…
Architecture and Design · Libraries or Frameworks
Use an input validation framework such as Struts or the OWASP ESAPI Validation API. Note that using a framework does not automatically address all input validation problems; be mindful of weaknesses that could arise from misusing the framework itself (CWE-1173).
Architecture and Design · Implementation · Attack Surface Reduction
Understand all the potential areas where untrusted inputs can enter the product, including but not limited to: parameters or arguments, cookies, anything read from the network, environment variables, reverse DNS lookups, query results, request headers, URL components, e-mail, files, filenames, databases, and any…
OWASP Cheat Sheets
XML Security (opens in a new tab)
Mass Assignment (opens in a new tab)
Injection Prevention (opens in a new tab)
SQL Injection Prevention (opens in a new tab)
Injection Prevention in Java (opens in a new tab)
OWASP ASVS 4.0.3
V13.3.1 (opens in a new tab)
V5.1.4 (opens in a new tab)
V5.1.3 (opens in a new tab)
Attack patterns
Buffer Overflow via Environment Variables (opens in a new tab)
Server Side Include (SSI) Injection (opens in a new tab)
Cross Zone Scripting (opens in a new tab)
Command Line Execution through SQL Injection (opens in a new tab)
Object Relational Mapping Injection (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

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Exploitation evidence

What to do

Description

AI analysisMachine-generated, updated 2 Dec 2025, 18:51 UTC

WeaknessesCWE-20

Technical details

References

Community reviews

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