FastGPT
CVE-2026-42343
MEDIUM
Attack ChainAIDerived
Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain
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AccessSend concurrent code-execution requests
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DeliveryExceed JavaScript worker pool capacity
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ExploitExhaust available workers
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ExecutionQueue legitimate requests without processing
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ImpactComplete service unavailability
Vulnerability Assessment
| Weakness |
CWE
OWASP Top 10:2025
A06:2025 - Insecure Design
ASVS 5.0
V2 - Validation and Business Logic Resource consumption limits
MITRE ATT&CK
1 technique across 1 tactic: Impact The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up. |
| Exploitation Conditions | The FastGPT code-sandbox API endpoint must be network-accessible without authentication (default configuration). Exploitation requires the ability to send HTTP requests to the code-execution API and knowledge of or ability to infer the JavaScript worker pool size. The attack succeeds because: (1) no OS-level resource isolation restricts worker memory or CPU, (2) the 500ms polling interval for soft limits is sufficiently long to be bypassed via timing attacks, (3) the application does not implement per-user or per-request rate limits, and (4) concurrent requests are processed in parallel without meaningful queuing or backpressure. The vulnerability is NOT limited to specific configurations or non-default setups-it affects any FastGPT deployment where the code-sandbox component is enabled and network-accessible, which is the standard deployment model for an AI Agent building platform. |
| Impact | CVSS 6.3 with attack vector Network and complexity Low indicates a remotely exploitable vulnerability. However, the CVSS vector includes AT:P (Attack Timing: Possible), suggesting the attack requires timing precision or specific conditions to succeed reliably. The impact vector (VA:L, SA:N) classifies this as availability damage without integrity or confidentiality impact, consistent with a denial-of-service attack. EPSS data is not provided, limiting probability assessment. KEV status is not confirmed in provided data, and no public exploit code has been reported at time of analysis. The real-world risk is significant for FastGPT deployments because: (1) the vulnerability is in a core sandbox component handling untrusted code, (2) no patch exists yet forcing reliance on workarounds, (3) attack complexity is low and requires no authentication, and (4) the impact is complete service unavailability rather than isolated user impact. For organizations operating FastGPT in production as an AI Agent building platform serving multiple users, this represents a critical availability risk despite the moderate CVSS score. |
| Exploit ScenarioDerived | An unauthenticated attacker sends a rapid burst of API requests to the code-sandbox endpoint, each containing a CPU-intensive JavaScript payload designed to execute longer than the 500ms polling interval. The soft memory limit is bypassed through timing attacks, and the JavaScript worker pool is exhausted as requests queue faster than workers complete execution. Subsequent legitimate user requests time out or fail, resulting in complete service denial. |
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Alert meAnalysisAIv1 – May 09, 2026
Denial of service vulnerability in FastGPT 4.14.13 and prior affects the code-sandbox component due to insufficient resource isolation and reliance on weak application-level memory limits. Unauthenticated remote attackers can trigger complete service unavailability by launching time-window memory attacks or exhausting the JavaScript worker pool via concurrent CPU-intensive requests. Attack complexity is reported as low with attack timing considerations (AT:P), and no vendor-released patch is available at time of publication.
RemediationAI
No vendor-released patch is available at time of publication. Immediate mitigation requires implementing OS-level resource constraints independent of the application: deploy FastGPT code-sandbox workers within cgroups-managed containers (Linux) with explicit CPU and memory quotas, use Kubernetes resource requests/limits to enforce pod-level constraints, or isolate workers in separate virtual machines with hard resource boundaries. Additionally, implement application-level rate limiting on code execution requests at the API layer to prevent concurrent request flooding, set strict timeouts (significantly shorter than 500ms) for code execution with immediate worker termination on timeout, and monitor JavaScript worker pool utilization with alerting on threshold breach. Disable or restrict access to the code-sandbox feature entirely if it is not critical to operations, as this provides the strongest mitigation. Each control has trade-offs: OS-level isolation reduces attack surface but increases infrastructure complexity and may increase latency; application-level rate limiting protects availability but may degrade user experience during legitimate load. Users should monitor the GitHub advisory at https://github.com/labring/FastGPT/security/advisories/GHSA-qv7v-r94x-6x3x for patch availability.
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Technical ContextAI · show details
FastGPT's code-sandbox component is responsible for safely executing user-provided code in an isolated environment. However, the implementation relies solely on application-level soft limits-specifically a 500ms polling interval for memory management-rather than OS-level resource constraints such as cgroups (Linux control groups) or kernel-level namespaces. This architectural approach leaves the service vulnerable to resource exhaustion attacks. CWE-400 (Uncontrolled Resource Consumption) identifies the root cause: the absence of proper rate limiting, quota enforcement, or hard resource boundaries allows attackers to consume memory and CPU beyond intended limits. The JavaScript worker pool, used to execute sandboxed code, can be completely exhausted by coordinated concurrent requests, leaving no capacity for legitimate users.
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Severity by source
CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N
Primary rating from Vendor (GitHub_M) · only source for this CVE.
CVSS VectorVendor: GitHub_M
Lifecycle Timeline
3DescriptionCVE.org
FastGPT is an AI Agent building platform. In versions 4.14.13 and prior, the code-sandbox component suffers from insufficient resource isolation and uncontrolled resource consumption. The service relies solely on an application-level soft limit (a 500ms polling interval) for memory management and lacks strict OS-level constraints such as cgroups or kernel-level namespaces. This architectural weakness allows attackers to easily bypass memory checks via time-window attacks, or exhaust the entire JavaScript worker pool via concurrent CPU-intensive requests, resulting in a complete Denial of Service (DoS) for legitimate users. At time of publication, there are no publicly available patches.
Threat Intelligence Overview
How this decision was reached
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What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
Denial of Service attacks render applications or systems unavailable by overwhelming resources or triggering failure conditions.
Affected Products (CPE)CVE.org
FastGPT is an AI Agent building platform. Prior to version 4.9.12, the LastRoute Parameter on login page is vulnerable t
Unauthenticated HTTP proxy abuse in FastGPT (AI Agent platform) prior to v4.14.9.5 allows remote attackers to relay arbi
Remote code execution in FastGPT's agent-sandbox component (versions 4.14.10 through 4.14.12) lets any network-adjacent,
NoSQL injection in FastGPT <4.14.9.5 password authentication allows unauthenticated remote attackers to bypass login con
Channel hijack and denial of service in FastGPT (labring/FastGPT) before 4.15.2 lets an unauthenticated remote attacker
GitHub Actions artifact-poisoning (pwn request) in labring/FastGPT allows an external attacker who opens a pull request
Authentication bypass in FastGPT 4.15.0-beta4 lets unauthenticated remote attackers forge JWTs and access internal plugi
NoSQL injection in FastGPT versions before 4.14.9.5 allows authenticated attackers to bypass password verification on th
FastGPT versions 4.14.8.3 and below contain a critical arbitrary code execution vulnerability in the fastgpt-preview-ima
Cross-tenant file disclosure in FastGPT prior to v4.15.0-beta5 allows an attacker to read another team's stored files by
Cross-tenant data disclosure in FastGPT 4.14.17 through 4.15.0-beta5 lets a low-privileged tenant user read another tena
Server-side request forgery in FastGPT before 4.15.0-beta4 lets an authenticated team member abuse the HTTP-tool OpenAPI
NVD
CVE.org
EPSS
Vulnrichment
GitHub
Exploit-DB