vLLM: ReDoS via structured_outputs.regex compiled without timeout in xgrammar and outlines backends
vllm-project · vllm
8.7HighCVSS 4.0
Recommended action
Within 7 days
High technical severity; prioritise exposed affected systems while verifying vendor guidance.
Patch available
Direct vendor intelligence
Authoritative vendor CSAF and VEX advisories
Structured product status and remediation from the issuing vendor. Product-state explanations are always visible; large lists can be searched or downloaded.
1 current
CVE-2026-55574 · CSAF 2.0 · revision 3 · finalRed Hat Product Securityvllm: vLLM: Denial of Service via adversarial regular expression in structured outputs API
15 known affected
The vendor explicitly identifies these products as affected by this CVE.
rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server
rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server
rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server
rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server
rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server
rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server
rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server
rhelai3/bootc-aws-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
rhelai3/bootc-azure-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
rhelai3/bootc-azure-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
rhelai3/bootc-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
Summary
A flaw was found in vLLM, a high-throughput and memory-efficient inference and serving engine for large language models (LLMs). A remote attacker could exploit this vulnerability by providing a specially crafted regular expression to the structured_outputs.regex API parameter. This adversarial regex, containing nested quantifiers, can cause an exponential expansion of the state-space in the grammar compiler, leading to an inference worker hanging indefinitely. This results in a Denial of Service (DoS) for the affected system.
Remediation
For more information visit https://access.redhat.com/errata/RHSA-2026:61627
Optional official sources
National CERT insights ?CERT means Computer Emergency Response Team; CSIRT is the closely related term Computer Security Incident Response Team.
Select the national-authority views to include. The exact source language is shown on each matched advisory. Your choice is remembered on this device and encoded in the shareable URL.
Official European source
ENISA European Vulnerability Database
Official EUVD identifiers, advisory evidence and known-exploited context. Missing fields are not treated as evidence of low risk.
1 current
ENISA EUVD identifier
EUVD-2026-41924
No EUVD known-exploited evidence
ENISA has published the identifier mapping but no EUVD description has been stored yet.
EUVD state
Present in the current official mapping
Known exploitation
Not present in the current ENISA EUVD known-exploited dataset. This is not proof of no exploitation.
ENISA score
Not supplied in the stored EUVD record
Advisory evidence
No linked advisory details stored yet
Recommended actionWithin 7 days
High technical severity; prioritise exposed affected systems while verifying vendor guidance.
Patch available
01
What, why and how
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Prior to 0.24.0, the structured_outputs.regex API parameter passes a user-supplied regular expression string directly to the grammar compiler backends with no compilation timeout; in the xgrammar backend the string reaches the regex compiler with no guard, and in the outlines backend the validation step blocks structural issues such as lookarounds and backreferences but performs no complexity analysis, so a pattern with nested quantifiers passes all checks and causes exponential state-space expansion, allowing a single request containing an adversarial regex to hang an inference worker indefinitely and deny service. This issue is fixed in version 0.24.0.
What
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Prior to 0.24.0, the structured_outputs.regex API parameter passes a user-supplied regular expression string directly to the grammar compiler backends with no compilation timeout; in the xgrammar backend the string reaches the regex compiler with no guard, and in the outlines backend the validation step blocks structural issues such as lookarounds and backreferences but performs no complexity analysis, so a pattern with nested quantifiers passes all checks and causes exponential state-space expansion, allowing a single request containing an adversarial regex to hang an inference worker indefinitely and deny service. This issue is fixed in version 0.24.0.
Why
The product uses a regular expression with a worst-case computational complexity that is inefficient and possibly exponential.
How
An attacker operating through a network path may attempt exploitation without authentication or user interaction. If successful, the issue may cause the confidentiality, integrity or availability impact described by the vendor.
What
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Prior to 0.24.0, the structured_outputs.regex API parameter passes a user-supplied regular expression string directly to the grammar compiler backends with no compilation timeout; in the xgrammar backend the string reaches the regex compiler with no guard, and in the outlines backend the validation step blocks structural issues such as lookarounds and backreferences but performs no complexity analysis, so a pattern with nested quantifiers passes all checks and causes exponential state-space expansion, allowing a single request containing an adversarial regex to hang an inference worker indefinitely and deny service. This issue is fixed in version 0.24.0.
Why
The product uses a regular expression with a worst-case computational complexity that is inefficient and possibly exponential.
How
An attacker operating through a network path may attempt exploitation without authentication or user interaction. If successful, the issue may cause the confidentiality, integrity or availability impact described by the vendor.
02
Exploit reality and attack path
CVSS severity, EPSS forecast probability, public exploit material and CISA-confirmed exploitation are separate signals.
Observed exploitation ?Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.No confirmed evidence
No CISA KEV match was present at the last successful refresh. This means no confirmation from that source, not proof of no exploitation.
Public PoC / exploit material ?Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.None recorded
No exploit-tagged reference or CISA SSVC proof-of-concept state is currently recorded. Research may still exist outside the structured feeds.
Likely attack path
a network path → Inefficient Regular Expression Complexity → cause the confidentiality, integrity or availability impact described by the vendor
Attack surface
Network
Privileges required
None: unauthenticated exploitation is possible
User interaction
None
Attack complexity
Low: no specialised conditions are recorded
Security boundary
Not a CVSS 4.0 base metric
Weakness ?CWE means Common Weakness Enumeration: a standard category for the underlying weakness.
CWE-1333: Inefficient Regular Expression Complexity. The product uses a regular expression with a worst-case computational complexity that is inefficient and possibly exponential.
CVSS vector ?CVSS means Common Vulnerability Scoring System. The vector records the metric values used to calculate technical severity.
Common Vulnerability Scoring System 4.0: the compact vector below is decoded into plain language.
AVNetworkAttack vector: The vulnerable component can be reached over a network.ACLowAttack complexity: No specialised conditions are required beyond attacker-controlled input.ATNoneAttack requirements: No additional deployment or execution condition is required.PRNonePrivileges required: The attacker does not need an account or existing privileges.UINoneUser interaction: No action by another user is required.VCNoneVulnerable-system confidentiality: No direct loss is represented by this metric.VINoneVulnerable-system integrity: No direct loss is represented by this metric.VAHighVulnerable-system availability: A successful attack can cause a major loss.SCNoneSubsequent-system confidentiality: No direct loss is represented by this metric.SINoneSubsequent-system integrity: No direct loss is represented by this metric.SANoneSubsequent-system availability: No direct loss is represented by this metric.
Post-exploitation / living off the land
No specific living-off-the-land technique is confirmed in the structured sources. Monitor normal administration tools for activity inconsistent with the affected service's baseline.
NetworkUnauthenticatedCWE-1333
A
Official authority intelligence
Only matched European and national findings are included. Language selectors and unavailable sources are omitted.
ENISA EUVD · EUVD-2026-41924Official EUVD mapping
0 linked advisory records.
Official EUVD record ↗BSI · German · WID-SEC-2026-2176vllm: Mehrere Schwachstellen ermöglichen Denial of Service
Ein Angreifer kann mehrere Schwachstellen in vllm ausnutzen, um einen Denial of Service Angriff durchzuführen.
Official advisory ↗Cyber Security Agency of Singapore · English · CSA-SB-20260708Security Bulletin 8 July 2026 [PDF, 1MB]]
The Cyber Security Agency of Singapore included this CVE in its official Security Bulletin 8 July 2026 [PDF, 1MB]], published on 8 July 2026. Open the linked bulletin for the product, severity and reference information published in that issue.
Official advisory ↗JVN iPedia · Japanese · JVNDB-2026-022823vLLMにおける非効率的な正規表現の複雑さに関する脆弱性
Operational remediation based on structured source evidence.
Status ?Patch availability is based on structured fixed-version fields and authoritative update references. If no fix is verified, check the vendor advisory before making a change.
For more information visit https://access.redhat.com/errata/RHSA-2026:61627
Workaround
No verified workaround is recorded. If business-safe, reduce exposure to the affected interface and allow only trusted sources until authoritative guidance is available.
04
Evidence and provenance
Published 6 Jul 2026 · Last source change 6 Jul 2026, 20:52 UTC · CWE-1333 · Inefficient Regular Expression Complexity
CVE recordCVE.org · 5.2
CVSS sourceCNA
EPSS source ?The date BlackTree first stored a score for this CVE from the daily FIRST EPSS feed.FIRST · tracked since 2026-08-14
European sourceENISA EUVD · EUVD-2026-41924
Product sourceVendor CSAF · Red Hat Product Security
Remediation sourceVendor CSAF · Red Hat Product Security
CWE sourceCNA
NVD statusNVD enriched
Core structured fields are present and their contributing authorities are shown above.
Affected versionsThe structured affected or fixed version information changed.
Before
rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; rhelai3/bootc-aws-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-azure-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-azure-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-gcp-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhoai/odh-vllm-gaudi-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · Fixed: registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x as a component of Red Hat AI Inference Server 3.3; registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le as a component of Red Hat AI Inference Server 3.3; registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64 as a component of Red Hat AI Inference Server 3.3
After
rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; rhelai3/bootc-aws-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-azure-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-azure-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-gcp-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhoai/odh-vllm-gaudi-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · Fixed: registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64 as a component of Red Hat AI Inference Server 3.2; registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64 as a component of Red Hat AI Inference Server 3.2; registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:7536efb184e4161d6b8a11477392b93613408c7d68daebb5cbad73b57a985e26_amd64 as a component of Red Hat AI Inference Server 3.2; registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x as a component of Red Hat AI Inference Server 3.3; registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le as a component of Red Hat AI Inference Server 3.3; registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64 as a component of Red Hat AI Inference Server 3.3
Technical terms and abbreviations used in this report
CVE
Common Vulnerabilities and Exposures: the public identifier for one disclosed vulnerability.
CVSS
Common Vulnerability Scoring System: a technical severity framework; it is not patching priority by itself.
EPSS
Exploit Prediction Scoring System: FIRST's estimate of the probability that exploitation activity will be observed in the next 30 days; it is a forecast, not confirmation.
CWE
Common Weakness Enumeration: the standard category describing the underlying software or hardware weakness.
CNA
CVE Numbering Authority: an organisation authorised to assign and publish CVE records.
CISA ADP
Cybersecurity and Infrastructure Security Agency Authorized Data Publisher: structured enrichment added to a CVE record.
NVD
National Vulnerability Database: NIST's enrichment service for CVE records.
CERT / CSIRT
A computer security incident response team that publishes warnings or coordinates incident response.
PoC
Proof of concept: public material that demonstrates or helps reproduce exploitation.
CSAF
Common Security Advisory Framework: a machine-readable format for security advisories.
LoTL
Living off the land: abuse of legitimate tools or system functions during an attack.
CVE-2026-55574: vLLM: ReDoS via structured_outputs.regex compiled without timeout in xgrammar and outlines backends | BlackTree
