PatchFirst · Vulnérabilités récentes
Vulnérabilités publiées pour Node.js au cours des 30 derniers jours : 387. Présentes dans le catalogue KEV : 0.
Signalements récents de vulnérabilités issus des annonces des éditeurs et de l’actualité de la sécurité. Ces alertes ne modifient pas la note de sécurité.
Selon GitHub Advisory Database, Node.js présente une vulnérabilité de déni de service (CVE-2026-109932). Les versions exactes affectées ne sont pas précisées. Le message ne précise pas de version corrigée.
Que faire maintenant : Vérifiez si une mise à jour de sécurité de Node.js contenant le correctif est disponible et installez-la.
CVE-2026-109932
Selon OpenNet, Node.js présente une vulnérabilité d’exécution de code à distance (CVE-2026-111497). Les versions exactes affectées ne sont pas précisées. Le message ne précise pas de version corrigée.
Que faire maintenant : Vérifiez si une mise à jour de sécurité de Node.js contenant le correctif est disponible et installez-la.
CVE-2026-111497
Vulnérabilités : 7797
OpenClaw before 2026.3.31 contains an authentication boundary vulnerability where Telegram legacy allowFrom migration incorrectly fans default-account trust into all named accounts. Attackers can exploit this trust propagation to bypass authentication controls and gain unauthorized access to named accounts.
OpenClaw before 2026.4.2 exposes configPath and stateDir metadata in Gateway connect success snapshots to non-admin authenticated clients. Non-admin clients can recover host-specific filesystem paths and deployment details, enabling host fingerprinting and facilitating chained attacks.
OpenClaw before 2026.3.31 contains a time-of-check-time-of-use vulnerability in sandbox file operations that allows attackers to bypass fd-based defenses. Attackers can exploit check-then-act patterns in apply_patch, remove, and mkdir operations to manipulate files between validation and execution.
OpenClaw before 2026.3.31 contains a callback origin mutation vulnerability in Plivo voice-call replay that allows attackers to mutate in-process callback origin before replay rejection. Attackers with captured valid callbacks for live calls can exploit this to manipulate callback origins during the replay process.
OpenClaw before 2026.3.31 allows workspace .env files to override the OPENCLAW_BUNDLED_HOOKS_DIR environment variable, enabling loading of attacker-controlled hook code. Attackers can replace trusted default-on bundled hooks from untrusted workspaces to execute arbitrary code.
OpenClaw before 2026.3.31 contains an information disclosure vulnerability in the Control Interface bootstrap JSON that exposes version and assistant agent identifiers. Attackers can extract sensitive fingerprinting information from the Control UI bootstrap payload to identify system versions and agent configurations.
OpenClaw before 2026.3.31 contains a decompression bomb vulnerability in image processing that fails to properly enforce pixel-limit guards on sips. Attackers can exploit this by uploading oversized images to cause denial of service through excessive memory consumption.
OpenClaw before 2026.3.31 contains an authentication rate limiting bypass vulnerability that allows attackers to circumvent shared authentication protections using fake device tokens. Attackers can exploit the mixed WebSocket authentication flow to bypass rate limiting controls and conduct brute force attacks against weak shared passwords.
OpenClaw before 2026.3.28 contains an environment variable sanitization vulnerability where GIT_TEMPLATE_DIR and AWS_CONFIG_FILE are not blocked in the host-env blocklist. Attackers can exploit approved exec requests to redirect git or AWS CLI behavior through attacker-controlled configuration files to execute untrusted code or load malicious credentials.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the GraphCypherQAChain node forwards user-provided input directly into the Cypher query execution pipeline without proper sanitization. An attacker can inject arbitrary Cypher commands that are executed on the underlying Neo4j database, enabling data exfiltration, modification, or deletion. This vulnerability is fixed in 3.1.0.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the specific flaw exists within the run method of the CSV_Agents class. The issue results from the lack of proper sandboxing when evaluating an LLM generated python script. An attacker can leverage this vulnerability to execute code in the context of the user running the server. Using prompt injection techniques, an unauthenticated attacker with the ability to send prompts to a chatflow using the CSV Agent node may convince an LLM to respond with a malicious python script that executes attacker controlled commands on the Flowise server. Th
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the specific flaw exists within the run method of the Airtable_Agents class. The issue results from the lack of proper sandboxing when evaluating an LLM generated python script. Using prompt injection techniques, an unauthenticated attacker with the ability to send prompts to a chatflow using the Airtable Agent node may convince an LLM to respond with a malicious python script that executes attacker controlled commands on the flowise server. This vulnerability is fixed in 3.1.0.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the text-to-speech generation endpoint (POST /api/v1/text-to-speech/generate) is whitelisted (no auth) and accepts a credentialId directly in the request body. When called without a chatflowId, the endpoint uses the provided credentialId to decrypt the stored credential (e.g., OpenAI or ElevenLabs API key) and generate speech. This vulnerability is fixed in 3.1.0.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the GET /api/v1/public-chatflows/:id endpoint returns the full chatflow object without sanitization for public chatflows. Docker validation revealed this is worse than initially assessed: the sanitizeFlowDataForPublicEndpoint function does NOT exist in the released v3.0.13 Docker image. Both public-chatflows AND public-chatbotConfig return completely raw flowData including credential IDs, plaintext API keys, and password-type fields. This vulnerability is fixed in 3.1.0.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, this vulnerability allows remote attackers to bypass authentication on affected installations of FlowiseAI Flowise. Authentication is not required to exploit this vulnerability. The specific flaw exists within the resetPassword method of the AccountService class. There is no check performed to ensure that a password reset token has actually been generated for a user account. By default the value of the reset token stored in a users account is null, or an empty string if they've reset their password before. An attacker with knowledge of the
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, a Mass Assignment vulnerability in the DocumentStore creation endpoint allows authenticated users to control the primary key (id) and internal state fields of DocumentStore entities. Because the service uses repository.save() with a client-supplied primary key, the POST create endpoint behaves as an implicit UPSERT operation. This enables overwriting existing DocumentStore objects. In multi-workspace or multi-tenant deployments, this can lead to cross-workspace object takeover and broken object-level authorization (IDOR), allowing an attac
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the password reset functionality on cloud.flowiseai.com sends a reset password link over the unsecured HTTP protocol instead of HTTPS. This behavior introduces the risk of a man-in-the-middle (MITM) attack, where an attacker on the same network as the user (e.g., public Wi-Fi) can intercept the reset link and gain unauthorized access to the victim’s account. This vulnerability is fixed in 3.1.0.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, Flowise contains an authentication bypass vulnerability that allows an unauthenticated attacker to obtain OAuth 2.0 access tokens associated with a public chatflow. By accessing a public chatflow configuration endpoint, an attacker can retrieve internal workflow data, including OAuth credential identifiers, which can then be used to refresh and obtain valid OAuth 2.0 access tokens without authentication. This vulnerability is fixed in 3.1.0.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, a Server-Side Request Forgery (SSRF) vulnerability exists in FlowiseAI's POST/GET API Chain components that allows unauthenticated attackers to force the server to make arbitrary HTTP requests to internal and external systems. By injecting malicious prompt templates, attackers can bypass the intended API documentation constraints and redirect requests to sensitive internal services, potentially leading to internal network reconnaissance and data exfiltration. This vulnerability is fixed in 3.1.0.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the core security wrappers (secureAxiosRequest and secureFetch) intended to prevent Server-Side Request Forgery (SSRF) contain multiple logic flaws. These flaws allow attackers to bypass the allow/deny lists via DNS Rebinding (Time-of-Check Time-of-Use) or by exploiting the default configuration which fails to enforce any deny list. This vulnerability is fixed in 3.1.0.
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