Description Summary:
A vulnerability in the vLLM inference engine allows for potential denial-of-service attacks due to inefficient regular expression complexity.

Executive Summary:
The vLLM inference engine is vulnerable to an algorithmic complexity attack that could allow unauthenticated remote attackers to cause a denial-of-service condition.

Vulnerability Details

CVE-ID: CVE-2026-55574

Affected Software: vLLM Project vLLM

Affected Versions: < 0.24.0

Vulnerability: This vulnerability (CWE-1333) stems from inefficient regular expression complexity within the vLLM engine. An unauthenticated attacker can supply specially crafted inputs that trigger catastrophic backtracking, leading to high resource consumption and denial-of-service.

Business Impact

Successful exploitation of this vulnerability can lead to significant system instability and denial-of-service, rendering the inference engine unresponsive to legitimate requests. Given the CVSS score of 8.7, this represents a high-severity risk to business continuity, particularly for organizations relying on vLLM for high-throughput AI workloads.

Remediation Plan

Immediate Action: Upgrade to vLLM version 0.24.0 or later immediately to resolve the regular expression inefficiency.

Proactive Monitoring: Monitor server CPU and memory utilization for sudden, unexplained spikes that correlate with incoming inference requests.

Compensating Controls: Implement strict input validation or request rate limiting at the API gateway layer to prevent the submission of malicious, overly complex input strings.

Exploitation Status

Public Exploit Available: false

Analyst Notes: As of July 7, 2026, there is no public information indicating active exploitation of this vulnerability. However, due to the nature of the flaw, the potential for exploitation is high.

Analyst Recommendation

This vulnerability presents a clear risk to the availability of AI serving infrastructure. Administrators must prioritize updating to version 0.24.0 to eliminate the underlying regex vulnerability. Prompt patching is essential to prevent potential service disruptions.

Description Summary:
A vulnerability in the vLLM inference engine allows unauthenticated attackers to trigger a denial-of-service via improper input validation of specified quantities.

Executive Summary:
An unauthenticated remote denial-of-service vulnerability exists in vLLM versions prior to 0.24.0, posing a significant risk to service availability.

Vulnerability Details

CVE-ID: CVE-2026-54234

Affected Software: vLLM Project vLLM

Affected Versions: < 0.24.0

Vulnerability: This vulnerability involves improper input validation and improper validation of specified quantities (CWE-20, CWE-1284), allowing an unauthenticated attacker to exhaust system resources and cause service disruption.

Business Impact

The CVSS score of 7.5 indicates a high severity rating due to the ease of exploitation over a network with no authentication required. Successful exploitation results in system downtime for the inference engine, which could disrupt AI-driven business processes and downstream applications relying on the vLLM service.

Remediation Plan

Immediate Action: Upgrade to vLLM version 0.24.0 or later immediately to incorporate the necessary input validation patches.

Proactive Monitoring: Monitor system resource metrics (CPU/Memory) and application logs for unusual spikes or error patterns indicative of resource exhaustion attacks.

Compensating Controls: Implement rate limiting and input size restrictions at the API gateway level to prevent malformed or excessively large requests from reaching the inference engine.

Exploitation Status

Public Exploit Available: false

Analyst Notes: As of July 7, 2026, there is no public information indicating active exploitation of this vulnerability. However, due to the nature of the flaw, the potential for exploitation is high.

Analyst Recommendation

Given that this vulnerability affects a core component of high-throughput inference engines and requires no authentication for exploitation, the risk to operational continuity is significant. Administrators should prioritize the deployment of the 0.24.0 update to ensure robust input handling and prevent denial-of-service conditions.

Description Summary:
A critical vulnerability exists within the vLLM inference and serving engine for large language models that may lead to significant security compromise.

Executive Summary:
The vLLM inference engine contains a high-severity vulnerability that could allow for unauthorized system impact if exploited by an attacker.

Vulnerability Details

CVE-ID: CVE-2026-54232

Affected Software: vLLM vLLM

Affected Versions: See vendor advisory for specific affected versions

Vulnerability: The vulnerability relates to the core functionality of the vLLM serving engine. Further technical specifics regarding the exact mechanism of the flaw are currently limited, necessitating close monitoring of vendor bulletins.

Business Impact

With a CVSS score of 8.8, this vulnerability poses a substantial risk to organizations deploying AI and LLM infrastructure. A successful exploit could lead to unauthorized access to the serving environment, data exfiltration, or denial-of-service conditions, severely impacting the availability and confidentiality of model-driven services.

Remediation Plan

Immediate Action: Check the official vLLM project repository or vendor security page for the latest updates and apply them immediately to the affected inference engines.

Proactive Monitoring: Review logs for unusual requests or patterns directed at the vLLM API endpoints that might indicate attempted exploitation.

Compensating Controls: Deploy a Web Application Firewall (WAF) or API gateway to filter incoming traffic and inspect requests for malicious payloads targeted at the inference engine.

Exploitation Status

Public Exploit Available: false

Analyst Notes: As of June 23, 2026, there is no public information indicating active exploitation of this vulnerability. However, due to the nature of the flaw, the potential for exploitation is high.

Analyst Recommendation

Due to the high CVSS score, this vulnerability should be treated with urgency. Security teams should prioritize patching vLLM instances and ensuring that the serving engine is isolated from public-facing exposure until the patch is successfully deployed and validated.

Description Summary:
The vllm library is vulnerable to path traversal, allowing attackers to potentially access or manipulate files outside of the intended directory.

Executive Summary:
A path traversal vulnerability in the vllm library poses a significant risk of unauthorized file access and potential system compromise.

Vulnerability Details

CVE-ID: CVE-2026-4944

Affected Software: vllm-project vllm

Affected Versions: unspecified through latest

Vulnerability: This vulnerability is a Path Traversal (CWE-22) issue, where improper limitation of a pathname allows unauthorized file system access. The attack vector is network-based and does not require authentication, though it may require user interaction.

Business Impact

Successful exploitation allows an attacker to read or potentially write arbitrary files on the host system, leading to full confidentiality, integrity, and availability impact. With a CVSS score of 8.8, this flaw represents a high-severity risk that could facilitate credential theft or further lateral movement within the infrastructure.

Remediation Plan

Immediate Action: Monitor official repository channels for a security patch; if no fix is available, restrict network access to the vllm service to trusted segments only.

Proactive Monitoring: Inspect system logs for unusual file access patterns or attempts to navigate directory structures outside of expected application paths.

Compensating Controls: Deploy a Web Application Firewall (WAF) or file system monitoring tools to detect and block path traversal sequences (e.g., ../) in incoming traffic.

Exploitation Status

Public Exploit Available: Unknown.

Analyst Notes: As of May 29, 2026, there is no confirmed active exploitation in the wild; however, per CISA's SSVC assessment a proof-of-concept exists, so exploitation risk should be treated as credible.

Analyst Recommendation

Given the high CVSS score and the existence of a proof-of-concept, users should prioritize isolating vulnerable instances from external networks. Organizations must monitor the vllm-project repository for release updates and apply the necessary patches as soon as they become available.

Description Summary:
A vulnerability in the vLLM inference engine allows unauthenticated users to bypass OpenAI API key requirements, potentially exposing LLM services to unauthorized access.

Executive Summary:
An authentication bypass vulnerability in vLLM allows unauthorized users to access LLM inference services without a valid API key, posing a significant risk of service abuse.

Vulnerability Details

CVE-ID: CVE-2026-48746

Affected Software: vLLM Project vLLM

Affected Versions: 0.3.0 through 0.22.0

Vulnerability: The flaw exists within the ASGI web server integration and the handling of the OpenAI API AuthenticationMiddleware, effectively allowing unauthenticated attackers to bypass API key verification.

Business Impact

The ability for unauthenticated parties to access inference services can lead to unauthorized consumption of expensive computational resources and potential data leakage from the LLM. Given the CVSS score of 9.1, this represents a critical risk to organizational budgets and data security, necessitating immediate remediation to prevent service exploitation.

Remediation Plan

Immediate Action: Upgrade to vLLM version 0.22.0 or later immediately to apply the necessary patches to the authentication middleware.

Proactive Monitoring: Review API access logs for anomalous request patterns or unauthorized traffic originating from unknown sources.

Compensating Controls: Implement network-level restrictions or a reverse proxy with secondary authentication mechanisms to control access to the vLLM endpoint until patching is complete.

Exploitation Status

Public Exploit Available: No

Analyst Notes: As of Jun 22, 2026, there is no public information indicating active exploitation of this vulnerability. However, due to the nature of the flaw, the potential for exploitation is high.

Analyst Recommendation

This vulnerability presents a severe risk by completely negating access control measures for your AI infrastructure. Organizations should prioritize the update to version 0.22.0 as a matter of urgency to restore secure API authentication.

Description Summary:
A security vulnerability exists within the vLLM inference and serving engine, potentially impacting the stability or security of LLM deployments.

Executive Summary:
A high-severity vulnerability in the vLLM inference engine could expose organizations to significant operational risk or unauthorized system interaction.

Vulnerability Details

CVE-ID: CVE-2026-41523

Affected Software: vLLM Inference Engine

Affected Versions: See vendor advisory for specific affected versions

Vulnerability: The vulnerability exists within the inference and serving logic of the vLLM engine. Without further technical details from the vendor, it is treated as a high-risk component flaw requiring immediate attention to ensure the integrity of LLM model serving.

Business Impact

The vLLM engine is a critical component for large language model deployments. Successful exploitation could lead to service disruption, unauthorized model manipulation, or potential data leakage, justifying the CVSS score of 7.5. The business impact is substantial for organizations relying on these engines for production AI services.

Remediation Plan

Immediate Action: Update the vLLM engine to the latest stable release as specified in the official vendor security advisory.

Proactive Monitoring: Monitor vLLM logs for abnormal API requests or unexpected resource consumption that could indicate an attempt to exploit the inference engine.

Compensating Controls: Restrict network access to the vLLM inference API to trusted internal services only, minimizing the attack surface.

Exploitation Status

Public Exploit Available: false

Analyst Notes: As of June 23, 2026, there is no public information indicating active exploitation of this vulnerability. However, due to the nature of the flaw, the potential for exploitation is high.

Analyst Recommendation

Security teams must prioritize updating the vLLM framework to mitigate this high-severity risk. Given the critical role of inference engines in AI infrastructure, failure to patch could lead to severe operational consequences; immediate verification of installed versions is advised.

Description Summary:
vLLM allows remote code execution because certain model files bypass the trust_remote_code security setting, forcing execution of remote code even when users have explicitly opted out.

Executive Summary:
The vLLM inference engine contains a critical security flaw that allows unauthenticated attackers to achieve remote code execution by bypassing user-defined security configurations.

Vulnerability Details

CVE-ID: CVE-2026-27893

Affected Software: vLLM Project vLLM

Affected Versions: >= 0.10.1, < 0.18.0

Vulnerability: This vulnerability is a protection mechanism failure where internal model implementation files hardcode the trust_remote_code=True parameter. This flaw allows an unauthenticated attacker to execute arbitrary code on the host system by loading a malicious model, effectively overriding the user-specified --trust-remote-code=False security opt-out.

Business Impact

The ability for an attacker to achieve remote code execution poses a severe threat to infrastructure integrity, as it grants full control over the server environment. Given the CVSS score of 8.8, this vulnerability presents a high risk of total system compromise, potential data exfiltration, and unauthorized access to proprietary machine learning models or sensitive environment variables.

Remediation Plan

Immediate Action: Update the vLLM package to version 0.18.0 or later to ensure the security opt-out mechanism is correctly enforced.

Proactive Monitoring: Monitor server logs for unexpected outbound network connections or processes spawned by the vLLM service that deviate from normal inference workloads.

Compensating Controls: Restrict access to the vLLM API to trusted internal networks and implement strict egress filtering to prevent the inference engine from reaching untrusted model repositories.

Exploitation Status

Public Exploit Available: No (Exploit_available: false).

Analyst Notes: As of March 28, 2026, there is no public information indicating active exploitation or a public proof-of-concept for this vulnerability. While the vulnerability is highly dangerous, it requires the user to load a malicious model repository, making it an exploit vector that relies on deceptive content delivery.

Analyst Recommendation

Organizations using vLLM for model serving must prioritize upgrading to version 0.18.0 immediately. Because this flaw bypasses explicit security controls, it undermines the trust model of the entire deployment, making the application of the vendor-provided patch the only effective way to restore system security.

Description Summary:
A Server-Side Request Forgery vulnerability exists in vLLM due to inconsistent URL parsing between the validation and request layers, allowing attackers to bypass SSRF protections.

Executive Summary:
A Server-Side Request Forgery vulnerability in vLLM versions 0.15.1 through 0.16.9 allows authenticated attackers to bypass security controls and potentially access internal network resources.

Vulnerability Details

CVE-ID: CVE-2026-25960

Affected Software: vLLM Project vLLM

Affected Versions: >= 0.15.1, < 0.17.0

Vulnerability: This is a Server-Side Request Forgery (CWE-918) vulnerability occurring in the load_from_url_async method. It stems from a discrepancy in how user-provided URLs are parsed by the validation library versus the aiohttp client, allowing an authenticated user to reach unintended internal endpoints.

Business Impact

The vulnerability carries a CVSS score of 7.1, indicating high severity. Successful exploitation allows an attacker to interact with internal services that are not exposed to the public internet, potentially leading to unauthorized data access or the exposure of sensitive internal infrastructure information. The risk is compounded by the role of vLLM in handling large language model deployments, which often reside in sensitive environments.

Remediation Plan

Immediate Action: Upgrade to vLLM version 0.17.0 or later to resolve the parsing inconsistency and patch the SSRF vulnerability.

Proactive Monitoring: Monitor application logs for unusual outbound connection patterns or requests directed toward internal IP addresses or private network segments originating from the vLLM instance.

Compensating Controls: Implement strict egress filtering on the server hosting vLLM to prevent unauthorized outbound connections to internal network segments, effectively neutralizing the impact of the SSRF.

Exploitation Status

Public Exploit Available: No — no confirmed public exploit exists.

Analyst Notes: As of March 10, 2026, there is no public information indicating active exploitation or a public proof-of-concept for this vulnerability. The flaw is technically significant because it demonstrates how subtle differences in dependency behavior can lead to security bypasses in modern network-aware applications.

Analyst Recommendation

Given the potential for internal network discovery and unauthorized interaction with sensitive services, organizations should prioritize the update to version 0.17.0. Verify all deployments of the vLLM engine and ensure that patching schedules are updated to include this version immediately to mitigate the risk of internal network exposure.

Description Summary:
A Server-Side Request Forgery vulnerability in the vLLM MediaConnector class allows authenticated attackers to bypass host restrictions and perform unauthorized internal network requests.

Executive Summary:
An SSRF vulnerability in vLLM versions prior to 0.14.1 allows authenticated attackers to perform unauthorized requests against internal network resources, risking data exposure and service disruption.

Vulnerability Details

CVE-ID: CVE-2026-24779

Affected Software: vLLM Project vLLM

Affected Versions: < 0.14.1

Vulnerability: The MediaConnector class fails to consistently enforce host name restrictions when processing media from user-provided URLs. By exploiting discrepancies in how different Python parsing libraries interpret backslashes, an authenticated attacker can coerce the vLLM server into making arbitrary requests to internal network segments.

Business Impact

Successful exploitation allows an attacker to interact with internal services that are otherwise protected from the public internet. This can lead to the exfiltration of sensitive data, unauthorized interaction with containerized management endpoints, or denial of service through manipulation of system metrics. With a CVSS score of 7.1, this vulnerability represents a significant risk to the integrity and availability of the internal infrastructure hosting the LLM engine.

Remediation Plan

Immediate Action: Update the vLLM package to version 0.14.1 or later immediately to incorporate the necessary input validation fixes in the MediaConnector class.

Proactive Monitoring: Review application access logs for unusual outbound request patterns originating from the vLLM service, particularly those targeting internal IP addresses or sensitive management ports.

Compensating Controls: Implement strict egress filtering on the network segment where vLLM containers are deployed to prevent the service from reaching unauthorized internal network resources.

Exploitation Status

Public Exploit Available: Unknown.

Analyst Notes: As of January 29, 2026, there is no confirmed active exploitation in the wild; however, per CISA's SSVC assessment a proof-of-concept exists, so exploitation risk should be treated as credible. The vulnerability is inherently dangerous in containerized environments where internal network visibility is often restricted for external services.

Analyst Recommendation

Given the potential for lateral movement within containerized environments, organizations should prioritize upgrading vLLM to version 0.14.1. Patching is the only effective way to remediate the underlying parsing discrepancy, and administrators should ensure that network security policies are configured to follow the principle of least privilege for all LLM inference workloads.

Description Summary:
vLLM allows arbitrary code execution by loading unverified dynamic modules during model resolution, which occurs before request handling and does not require authentication.

Executive Summary:
An unauthenticated remote code execution vulnerability in vLLM allows attackers to execute arbitrary code on the host server by providing a malicious model repository path.

Vulnerability Details

CVE-ID: CVE-2026-22807

Affected Software: vLLM Project vLLM

Affected Versions: >= 0.10.1, < 0.14.0

Vulnerability: The software improperly handles Hugging Face auto_map dynamic modules during model resolution by failing to gate them with trust_remote_code. This flaw allows an unauthenticated attacker to trigger arbitrary code execution on the host system during the model loading process.

Business Impact

Successful exploitation of this vulnerability grants an attacker full control over the host server, leading to complete system compromise. Given the CVSS score of 8.8, this represents a critical risk that could result in the exfiltration of sensitive model data, unauthorized access to the underlying infrastructure, or the deployment of persistent malware.

Remediation Plan

Immediate Action: Update the vLLM package to version 0.14.0 or later immediately to resolve the improper code execution path.

Proactive Monitoring: Review server logs for unusual model loading activities or unexpected process spawns occurring during the initialization phase.

Compensating Controls: Restrict access to the vLLM environment to trusted networks and ensure that model repositories are sourced only from verified and secure locations to prevent the loading of malicious code.

Exploitation Status

Public Exploit Available: Yes — a public proof-of-concept repository exists on GitHub.

Analyst Notes: As of January 23, 2026, there is no public information indicating active exploitation in the wild. However, the availability of a public proof-of-concept makes the risk of exploitation credible and significant. The vulnerability is inherently dangerous because it executes code before the application begins handling any network requests, bypassing traditional application-level authentication.

Analyst Recommendation

The severity of this vulnerability necessitates immediate action, as it provides a direct path to full system compromise without requiring user credentials. Organizations running vLLM within the affected version range must prioritize upgrading to version 0.14.0 to neutralize the risk. Failure to patch will leave the infrastructure exposed to trivial remote code execution attacks.

Description Summary:
A heap address leak in the vLLM multimodal endpoint allows attackers to bypass ASLR, enabling potential remote code execution when chained with a secondary heap overflow vulnerability.

Executive Summary:
An unauthenticated heap address leak in vLLM versions 0.8.3 through 0.14.0 allows for potential remote code execution by bypassing Address Space Layout Randomization (ASLR).

Vulnerability Details

CVE-ID: CVE-2026-22778

Affected Software: vLLM Project vLLM

Affected Versions: 0.8.3 through 0.14.0

Vulnerability: The vulnerability exists in the vLLM multimodal endpoint, where processing an invalid image triggers a verbose error from the PIL library that leaks heap memory addresses to the client. This information disclosure significantly weakens ASLR protections, allowing an unauthenticated attacker to facilitate a heap overflow attack against the JPEG2000 decoder.

Business Impact

The exploitation of this vulnerability poses a critical risk to the integrity and availability of AI inference infrastructure. By achieving remote code execution, an attacker could gain full control over the serving engine, leading to data exfiltration of sensitive model inputs or unauthorized access to the host environment. The CVSS score of 9.8 reflects the high severity of this flaw, as it is network-exploitable without requiring authentication or user interaction.

Remediation Plan

Immediate Action: Upgrade the vLLM package to version 0.14.1 or later immediately to resolve the memory leak vulnerability.

Proactive Monitoring: Review server logs for anomalous error messages, particularly those related to PIL processing or unexpected heap address output, which may indicate reconnaissance or exploitation attempts.

Compensating Controls: Implement a Web Application Firewall (WAF) or input validation layer to sanitize multimodal requests and reject malformed image files before they reach the vLLM processing pipeline.

Exploitation Status

Public Exploit Available: No (a Nuclei detection template exists, but there is no evidence of a weaponized exploit or public proof-of-concept).

Analyst Notes: As of Feb 2, 2026, there is no public information indicating active exploitation or a public proof-of-concept for this vulnerability. While the vulnerability is highly technical and requires chaining with a secondary flaw to achieve code execution, the ability to bypass ASLR makes it a significant target for attackers.

Analyst Recommendation

Given the critical nature of this vulnerability and the potential for remote code execution, organizations utilizing vLLM for model inference must prioritize patching. Update to version 0.14.1 immediately to eliminate the information disclosure vector. Ensure that any public-facing inference endpoints are strictly monitored for unusual request patterns until the update is applied across the environment.

Description Summary:
A code injection vulnerability in vLLM allows unauthorized execution of remote Python code during model configuration loading, bypassing trust settings.

Executive Summary:
The vLLM inference engine contains a critical remote code execution vulnerability that allows an attacker to execute arbitrary Python code on the host system by providing a malicious model configuration.

Vulnerability Details

CVE-ID: CVE-2025-66448

Affected Software: vLLM Project vLLM

Affected Versions: < 0.11.1

Vulnerability: This vulnerability involves improper control of code generation (CWE-94) within the Nemotron_Nano_VL_Config class. The application automatically resolves and instantiates classes via auto_map entries in model configurations, which triggers remote Python code execution even when the user has explicitly disabled remote code trust.

Business Impact

The ability for an unauthenticated or low-privileged attacker to achieve remote code execution on an inference server poses a catastrophic risk to organizational data and infrastructure. Given the CVSS score of 7.1, this flaw could lead to full system compromise, unauthorized access to sensitive large language model data, and potential lateral movement within the network.

Remediation Plan

Immediate Action: Update the vLLM package to version 0.11.1 or later immediately to resolve the insecure configuration loading logic.

Proactive Monitoring: Review system logs for unexpected outbound network connections initiated by the vLLM process, as these may indicate attempts to fetch and execute external malicious repositories.

Compensating Controls: Implement strict egress filtering on the inference server to prevent unauthorized connections to unknown repositories until the update can be applied.

Exploitation Status

Public Exploit Available: Unknown

Analyst Notes: As of December 1, 2025, there is no public information indicating active exploitation or a published proof-of-concept for this vulnerability. While the vulnerability requires specific configuration loading conditions, the inherent mechanism of silently executing code from remote repositories makes it a high-risk target for supply chain style attacks.

Analyst Recommendation

This vulnerability represents a significant security oversight in how vLLM handles external model configurations. Security teams must prioritize upgrading to version 0.11.1 to close this execution vector. Failure to patch allows attackers to bypass security settings intended to prevent remote code execution, making immediate remediation essential to maintaining the integrity of the AI infrastructure.

Description Summary:
A memory corruption vulnerability in the vLLM Completions API endpoint allows authenticated users to trigger a denial-of-service or potential remote code execution via malicious tensor payloads.

Executive Summary:
A critical memory corruption vulnerability in the vLLM inference engine allows authenticated attackers to execute arbitrary code or crash the service.

Vulnerability Details

CVE-ID: CVE-2025-62164

Affected Software: vLLM Project vLLM

Affected Versions: v0.10.2 through v0.11.0

Vulnerability: The vulnerability stems from the use of unsafe deserialization via torch.load() on user-supplied prompt embeddings within the Completions API. Authenticated attackers can leverage this to trigger an out-of-bounds memory write during tensor processing, leading to service crashes or potential remote code execution.

Business Impact

Successful exploitation of this flaw poses a severe risk to infrastructure integrity and data confidentiality. Given the CVSS score of 8.8, this vulnerability allows an authenticated attacker to gain significant control over the inference server, potentially leading to unauthorized access to sensitive model data or full system compromise. The ability to execute arbitrary code in a high-performance serving environment could facilitate lateral movement within the network.

Remediation Plan

Immediate Action: Update the vLLM package to version 0.11.1 or later immediately to incorporate the necessary input validation and tensor integrity checks.

Proactive Monitoring: Monitor API logs for anomalous request patterns, particularly those involving large or malformed serialized tensor payloads directed at the Completions endpoint.

Compensating Controls: Implement strict network access controls to limit which users can interact with the Completions API, and utilize an application firewall to inspect incoming payloads for suspicious serialization patterns if immediate patching is not feasible.

Exploitation Status

Public Exploit Available: No (exploit_available: false)

Analyst Notes: As of November 24, 2025, there is no public information indicating active exploitation or a public proof-of-concept for this vulnerability. The flaw is inherently dangerous due to the combination of insecure deserialization and the direct processing of untrusted tensor data in a privileged execution context.

Analyst Recommendation

The severity of this vulnerability necessitates an immediate update to version 0.11.1. Organizations utilizing vLLM for model inference should prioritize this remediation to prevent potential remote code execution and service disruption. Failure to patch leaves the inference infrastructure exposed to sophisticated exploitation attempts targeting the model serving pipeline.

Description Summary:
vLLM API key validation is vulnerable to a timing attack, which allows attackers to infer valid API keys character by character through repeated requests.

Executive Summary:
A critical authentication vulnerability in vLLM before version 0.11.0rc2 allows remote, unauthenticated attackers to bypass API key security via timing-based side-channel analysis.

Vulnerability Details

CVE-ID: CVE-2025-59425

Affected Software: vLLM Project vLLM

Affected Versions: < 0.11.0rc2

Vulnerability: The vulnerability exists in the API key validation logic, which utilizes a non constant time string comparison. This flaw allows an unauthenticated attacker to observe response time variations to iteratively guess valid API keys.

Business Impact

The exploitation of this vulnerability directly undermines the authentication mechanism of the vLLM inference engine. Successful unauthorized access could lead to the exposure of sensitive model data, unauthorized use of expensive compute resources, or potential manipulation of model inputs, resulting in significant operational and security risks. With a CVSS score of 7.5, this high-severity flaw requires immediate remediation to prevent unauthorized system access.

Remediation Plan

Immediate Action: Update the vLLM installation to version 0.11.0 or later to implement constant-time string comparison for API key validation.

Proactive Monitoring: Monitor API server access logs for anomalous patterns of high-frequency requests targeting the authentication endpoint, which may indicate an ongoing timing attack.

Compensating Controls: Deploy a Web Application Firewall (WAF) or API gateway to implement rate limiting on authentication requests, which increases the difficulty of performing the necessary volume of requests for a timing attack.

Exploitation Status

Public Exploit Available: Unknown.

Analyst Notes: As of October 8, 2025, there is no public information indicating active exploitation or a public proof-of-concept for this vulnerability. While exploitation is technically feasible through statistical analysis, it requires a significant number of requests to succeed, making it a sophisticated but preventable threat.

Analyst Recommendation

Given the ease with which this vulnerability can be exploited by an unauthenticated attacker, organizations must prioritize patching their vLLM deployments. Updating to version 0.11.0 effectively mitigates the risk by introducing secure comparison methods. Organizations unable to update immediately should implement strict rate limiting and monitor for suspicious authentication traffic to reduce the likelihood of successful exploitation.

Description Summary:
A denial of service vulnerability in vLLM allows unauthenticated remote attackers to cause server memory exhaustion and crashes by sending an HTTP GET request with an excessively large header.

Executive Summary:
An unauthenticated denial of service vulnerability in vLLM can be exploited by remote attackers to crash inference engines via memory exhaustion.

Vulnerability Details

CVE-ID: CVE-2025-48956

Affected Software: vLLM Project vLLM

Affected Versions: >= 0.1.0, < 0.10.1.1

Vulnerability: This is a resource consumption vulnerability (CWE-400) where the application fails to properly limit the size of incoming HTTP headers. An unauthenticated remote attacker can trigger this vulnerability by sending a single malicious GET request, forcing the server to exhaust its available memory.

Business Impact

Successful exploitation of this vulnerability results in service disruption, as the inference engine will likely crash or become unresponsive. Given the CVSS score of 7.5, this high-severity flaw poses a significant risk to the availability of AI-driven applications and services that rely on vLLM for model serving, potentially leading to operational downtime and business process interruptions.

Remediation Plan

Immediate Action: Update the vLLM package to version 0.10.1.1 or later immediately to incorporate the necessary resource handling fixes.

Proactive Monitoring: Monitor server resource utilization logs for unusual memory spikes or frequent service restarts that may indicate attempted exploitation.

Compensating Controls: Implement a Web Application Firewall (WAF) or an API gateway in front of the vLLM service to enforce strict limits on incoming request header sizes and reject oversized requests.

Exploitation Status

Public Exploit Available: Unknown.

Analyst Notes: As of August 23, 2025, there is no public information indicating active exploitation or a published proof-of-concept for this vulnerability. The flaw is inherently dangerous due to its simplicity, as it requires no authentication and only a single crafted request to achieve impact.

Analyst Recommendation

This vulnerability represents a critical threat to service availability for organizations deploying vLLM. IT and security teams must prioritize the update to version 0.10.1.1 as soon as possible. In environments where immediate patching is not feasible, deploying edge-level request filtering is essential to prevent potential service outages caused by malicious actors.