Leaked LLM Credentials
Leaked LLM Credentials, in the context of cybersecurity, are a specific type of credential leak involving the secret keys, tokens, or service account passwords necessary to authenticate and gain programmatic access to Large Language Models (LLMs) and other proprietary Generative AI services.
These credentials represent the digital identity of a user or an application authorized to interact with the LLM API. The leakage occurs when sensitive secrets are unintentionally made public, often due to human error or misconfiguration.
The exposure of LLM credentials poses a high-severity risk because it allows an external attacker to:
Financial Abuse and Denial of Service: An attacker can use the compromised key to execute costly inference calls against the LLM provider, resulting in massive, fraudulent billing for the legitimate account holder. They may also swamp the service with requests, causing denial-of-service attacks against genuine users.
Intellectual Property Theft and Exfiltration: The attacker can run targeted query attacks—such as model extraction or membership inference—to probe the LLM's behavior, potentially revealing proprietary model logic or characteristics of the private training data.
Prompt Injection and Model Misuse: If the leaked credential grants access to a custom-tuned or proprietary LLM endpoint, the attacker can use it to test and execute sophisticated prompt-injection attacks against the model, potentially causing the model to generate harmful content or violate internal policies.
Systemic Compromise: In some cases, the credential may be a high-privilege service account key that not only accesses the LLM but also the underlying cloud infrastructure (like storage or compute resources), leading to a much broader system breach.
The continuous discovery and revocation of leaked LLM credentials is a critical priority for organizations to prevent unauthorized usage and protect their AI investments.
ThreatNG, which is an all-in-one external attack surface management, digital risk protection, and security ratings solution, provides essential external vigilance against the risk of Leaked LLM Credentials by continuously searching the public-facing internet for these highly critical secrets. ThreatNG operates from the perspective of an unauthenticated attacker to identify exposure before it can be used to compromise the GenAI service or infrastructure.
External Discovery and Inventory
ThreatNG’s foundational capability is its purely external, unauthenticated discovery with no connectors, which is critical for identifying the platforms and channels where LLM credentials are typically leaked.
Technology Stack Identification: ThreatNG uncovers nearly 4,000 technologies, detailing specific vendors used, including hundreds of technologies categorized as Artificial Intelligence, as well as vendors in AI Model & Platform Providers and AI Development & MLOps. Discovering these technologies on an exposed subdomain provides context to any leaked key that is found, confirming it is an LLM-related credential.
Subdomain Intelligence: ThreatNG uncovers subdomains and identifies the cloud and web platforms hosting them. This can identify exposed environments where keys might be stored in unsecure configuration files.
Example of ThreatNG Helping: ThreatNG discovers an unmanaged subdomain running a technology identified as an AI Model & Platform Provider vendor. This initial discovery provides the security team with a targeted scope for searching for related credential leaks.
External Assessment for Credential Risk
ThreatNG's security ratings and assessment modules are explicitly designed to find and prioritize the external exposure of secrets, which includes LLM credentials.
Non-Human Identity (NHI) Exposure: This is a critical governance metric that quantifies an organization's vulnerability to threats from high-privilege machine identities, such as leaked API keys and service accounts. The discovery of a leaked LLM credential directly contributes to a high NHI Exposure rating.
Cyber Risk Exposure (Sensitive Code): This rating is based on findings that include Sensitive Code Discovery and Exposure (code secret exposure). Finding a publicly exposed configuration file with an LLM key via this assessment confirms a critical external vulnerability.
Data Leak Susceptibility: This rating is derived from uncovering external digital risks across Cloud Exposure and Compromised Credentials. A leaked LLM credential often provides the attacker with a path to exfiltrate proprietary data, thus contributing to the data leak risk.
Example of ThreatNG Helping: ThreatNG flags a high Cyber Risk Exposure rating because its Sensitive Code Exposure investigation module uncovered an AWS Access Key ID in a public code repository. An attacker could use this key to access the AI's cloud infrastructure, leading to financial abuse or model theft.
Reporting and Continuous Monitoring
ThreatNG provides Continuous Monitoring of the external attack surface and digital risk, ensuring that the leak of an LLM credential is flagged immediately.
Reporting (Security Ratings): The Non-Human Identity (NHI) Exposure rating (A-F scale) provides an easy-to-understand metric for executives to grasp the severity of leaked LLM credentials.
External Adversary View and MITRE ATT&CK Mapping: ThreatNG automatically translates raw findings—like leaked credentials—to specific MITRE ATT&CK techniques (e.g., initial access), showing exactly how an adversary could exploit the leaked key and allowing security leaders to prioritize based on likely exploitation.
Investigation Modules
ThreatNG's Investigation Modules are specifically designed for digital risk protection, targeting the key leakage channels where LLM credentials are found.
Sensitive Code Exposure (Code Repository Exposure): This module discovers public code repositories and looks explicitly for Access Credentials, including various API Keys (e.g., Google OAuth Key, Stripe API Key), Access Tokens, and Configuration Files. This is the most direct way to find leaked LLM credentials.
Online Sharing Exposure: This module identifies the presence of organizational entities on code-sharing platforms such as Pastebin and GitHub Gist. Developers often paste configuration snippets containing keys here, making this a critical area for discovery.
Mobile App Exposure: This module evaluates the exposure of an organization’s mobile apps and the presence of Access Credentials (including APIs, Amazon AWS Access Key ID, and various OAuth credentials) and Security Credentials within them.
Example of ThreatNG Helping: An analyst uses the Sensitive Code Exposure module and finds a Gist containing an exposed Twilio API Key and a configuration file with an endpoint that is confirmed to be an AI communication service. This immediate, external discovery allows the security team to revoke the key and prevent fraudulent use of the AI service.
Complementary Solutions
ThreatNG's external discovery of leaked LLM credentials provides the definitive external evidence needed to trigger automated internal security responses.
Complementary Solutions (Secrets Management Platforms): ThreatNG's discovery of a leaked service account credential via NHI Exposure provides external, irrefutable proof of compromise. This finding is routed to the Secrets Management platform, triggering an automated workflow to revoke the exposed key and tighten the access permissions for all remaining keys accessing AI infrastructure.
Complementary Solutions (Security Orchestration, Automation, and Response (SOAR) Systems): When ThreatNG identifies a leaked LLM key in a public code repository, the finding can trigger a SOAR playbook. The SOAR system automatically generates a ticket for the DevOps team, emails the developer responsible (if traceable), and initiates a high-priority, authenticated internal scan for other keys in the same repository.
Complementary Solutions (Cloud Security Posture Management (CSPM) Platforms): ThreatNG’s discovery of a leaked cloud access key via Cyber Risk Exposure can be used by the CSPM platform to prioritize internal policy enforcement. The CSPM can use the external finding to immediately audit all IAM policies associated with that key's account, ensuring that the principle of least privilege is strictly enforced before the key is used to compromise the AI environment.
