Unauthenticated Prompt Injection Vector
The Unauthenticated Prompt Injection Vector is a term used in cybersecurity to define a specific type of vulnerability where an attacker can successfully carry out a Prompt Injection attack on a Large Language Model (LLM) application without needing first to gain any form of authorized access—meaning, they don't need credentials, a valid user session, or an API key.
It represents the most immediate and easily exploitable external threat to an LLM application.
Detailed Breakdown of the Vector
The vector is not the attack itself (the malicious prompt), but rather the public-facing, unhardened entry point that enables the attack. This vulnerability exists because of a breakdown in the security perimeter around the LLM's interface.
Nature of the Access: The core issue is that the attacker can submit input (the prompt) to the LLM's endpoint (usually an API or web chatbot) with no initial gatekeeping. This access is unauthenticated, meaning the system cannot verify the user's identity or authorization level.
Common Causes of Exposure: This vector arises from several key misconfigurations:
Exposed Endpoints: A model API is left open to the public internet without requiring an authentication token (like an API key or bearer token).
Leaked Credentials: Even if the API requires a key, that key or a service account credential is found in a publicly accessible location (like a GitHub repository, a cloud bucket, or a Pastebin post). An attacker can then use this stolen key to become a seemingly "authenticated" user without having gone through the legitimate sign-up process, effectively bypassing the security control from the outside.
Missing Rate Limiting: Without authentication, there is often no effective rate limiting or user quota enforcement, allowing attackers to run unlimited, unmonitored queries.
Cybersecurity Implication: The presence of an unauthenticated vector means an attacker can achieve maximum impact with minimal effort. This vector enables several high-risk outcomes:
Model Theft: Without throttling, an attacker can rapidly submit queries to reconstruct the model's proprietary logic (Model Extraction).
Sensitive Data Disclosure: The attacker can execute a Prompt Injection attack to trick the model into revealing internal system prompts, database information, or sensitive training data.
Denial of Service (DoS) / Denial of Wallet (DoW): The attacker can easily flood the unthrottled endpoint with resource-intensive queries, causing service crashes or unsustainable billing costs.
The Unauthenticated Prompt Injection Vector is a foundational external exposure because fixing it requires addressing external security controls (authentication, credential management, cloud exposure) before internal model-level defenses (like prompt filtering) can even be tested.
ThreatNG's capabilities are tuned explicitly for Unauthenticated AI Discovery, making it the ideal solution for identifying and mitigating the Unauthenticated Prompt Injection Vector before an attacker can exploit it. This vector, which involves attacking an LLM endpoint without credentials, is addressed by ThreatNG's focus on external exposure and leaked secrets.
External Discovery
ThreatNG's External Discovery module eliminates the blind spot of unauthenticated access by conducting a purely external, unauthenticated search across the organization's entire digital footprint.
How it helps: The core issue of an Unauthenticated Prompt Injection Vector is the existence of an accessible AI endpoint. ThreatNG uses Technology Stack Identification to exhaustively discover all exposed technologies, including those categorized as Artificial Intelligence, down to specific vendors like OpenAI or Hugging Face. This confirms the existence of the vulnerable public endpoint.
Example of ThreatNG helping: ThreatNG identifies a subdomain, api-chatbot-prod.company.com, running a technology identified as an AI Model & Platform Provider service. This discovery immediately confirms the presence of an externally accessible, unauthenticated AI asset, a key component of the Unauthenticated Prompt Injection Vector.
External Assessment
ThreatNG’s external assessment modules immediately check the discovered endpoints for the configuration flaws that permit unauthenticated attacks.
Highlight and Examples:
Leaked Credentials (Bypassing Authentication): The Non-Human Identity (NHI) Exposure Security Rating is a critical governance metric that quantifies the vulnerability posed by leaked API keys and service accounts. When revealed, these credentials provide the attacker with the "key" to exploit the vector.
Example: The Sensitive Code Discovery and Exposure capability scans public code repositories and mobile apps for leaked Access Credentials (e.g., Google Cloud API Keys, Authorization Bearer tokens). If ThreatNG finds a leaked LLM access token, it provides Legal-Grade Attribution, turning a chaotic finding into irrefutable evidence of a compromised authentication mechanism that fuels the unauthenticated attack vector.
Endpoint Configuration Flaws: The Cyber Risk Exposure rating flags infrastructure misconfigurations that enable unauthenticated attackers.
Example: Subdomain Intelligence checks exposed ports and headers. If the newly discovered AI endpoint is found to have Exposed Ports (e.g., a database port left open) or is missing security headers (Content-Security-Policy), ThreatNG flags a poor security posture that makes the unauthenticated attack vector more severe.
Continuous Monitoring
Continuous Monitoring of the external attack surface, digital risk, and security ratings ensures that a secure AI endpoint does not suddenly become an unauthenticated injection vector.
How it helps: If a developer accidentally removes the API key requirement from a deployment configuration, or if an expired key is pushed to a public repository, continuous monitoring immediately detects this configuration drift. This swift detection minimizes the time the unauthenticated prompt injection vector is exposed.
Investigation Modules
The investigation modules provide the necessary forensic detail and context needed to understand and address the external vector.
Highlight and Examples:
Online Sharing Exposure: This module identifies an organization's presence on platforms such as Pastebin and GitHub Gist.
Example: ThreatNG finds a configuration snippet on a development forum that references the specific, live URL of the unauthenticated AI endpoint. This finding from the unmonitored conversational attack surface provides the attacker with the exact target needed to exploit the vector.
External Adversary View/MITRE ATT&CK Mapping: ThreatNG automatically correlates raw findings with MITRE ATT&CK techniques, providing a strategic narrative.
Example: The discovery of an exposed API key (via Sensitive Code Exposure) is mapped to Initial Access techniques. This demonstrates how the exposed key could be used to compromise the LLM agent and execute a subsequent injection attack, directly tying the external vulnerability to the attacker’s method.
Intelligence Repositories
ThreatNG’s Intelligence Repositories (DarCache) provide validation and prioritization for the unauthenticated vector findings.
How it helps: The Vulnerabilities (DarCache Vulnerability) repository integrates KEV (Known Exploited Vulnerabilities) data. If the software hosting the discovered unauthenticated AI endpoint has a known vulnerability, the KEV data confirms it as an immediate and proven threat, ensuring the fix for that infrastructure is prioritized to eliminate the vector.
Cooperation with Complementary Solutions
ThreatNG's external intelligence is essential for activating internal security controls to eliminate the vector's underlying causes.
Cooperation with Secrets Management Platforms: ThreatNG identifies leaked credentials from the external attack surface.
Example: ThreatNG finds an exposed API Key that could be used to gain unauthorized access to the AI service. This external finding is instantly routed to a complementary Secrets Management Platform, which automatically triggers the revocation or rotation of the compromised key, eliminating the means for an unauthenticated attacker to bypass the authentication control.
Cooperation with API Security Gateways: ThreatNG identifies exposed external interfaces.
Example: ThreatNG's Subdomain Intelligence discovers a newly exposed GenAI API endpoint. This external discovery is immediately routed to a complementary API Security Gateway, forcing the gateway to implement essential security policies, such as rate limiting and enhanced input validation, on that specific external endpoint to prevent the high-volume query submission required for the unauthenticated prompt injection attack.
