Leaked AI Agent Credentials
Leaked AI Agent Credentials are a specific and critical cybersecurity risk that falls under the umbrella of Non-Human Identity (NHI) exposure. It refers to the unauthorized public disclosure of the unique digital keys, tokens, or service account credentials used by an AI agent or Large Language Model (LLM) application to perform actions or access downstream corporate systems.
Detailed Breakdown of the Risk
AI agents—autonomous or semi-autonomous LLM systems—require these credentials because they are designed to perform functions beyond simple text generation, such as:
Reading files from a corporate data store.
Sending emails or messages.
Executing code via an external API.
Accessing a private vector database.
When these machine credentials are leaked, they become a high-value target for attackers because they grant immediate, unauthorized access with potentially excessive permissions.
Nature of the Credentials: These are typically non-human secrets, such as:
API Keys or Tokens (e.g., LLM provider keys, Heroku keys, Stripe keys).
Cloud Credentials (e.g., AWS Access Key IDs, Google Cloud Service Account Keys).
Database Passwords or Connection Strings.
Mechanisms of Leakage: The leakage almost always occurs due to developer error or misconfiguration, exposing the secret externally:
Public Code Repositories: A developer accidentally commits a credential to a public GitHub or GitLab repository.
Public Cloud Storage: Configuration files containing credentials are left in a publicly exposed cloud storage bucket.
Mobile App Decompilation: Secrets are hardcoded into a mobile application's binary, which can be easily extracted via reverse engineering.
Development Forums: A developer pastes a configuration snippet into a public help forum (like Pastebin) to ask a question.
Cybersecurity Implications (Enabling Excessive Agency): The leakage of an AI agent's credentials is the direct enabler of high-impact attacks because it bypasses security controls:
Privilege Escalation: If the leaked credential belongs to a high-privileged service account (a common occurrence for convenience), an attacker gains full administrative access to the connected systems, violating the principle of least privilege.
Data Exfiltration: The attacker can use the stolen credentials to command the AI agent to read sensitive data (e.g., customer files, financial records) and forward it outside the network, often enabling LLM06:2025 Excessive Agency or LLM02:2025 Sensitive Information Disclosure.
System Takeover: The attacker can use the key to tamper with the AI model or its hosting infrastructure, potentially executing arbitrary code or embedding backdoors.
Effectively, Leaked AI Agent Credentials transform a security risk from a model behavior problem into a fundamental identity and access management failure, providing the attacker with a powerful, unmonitored identity to perform malicious actions.
ThreatNG provides extensive capabilities to identify and mitigate the risks posed by Leaked AI Agent Credentials through continuous, unauthenticated monitoring of the external attack surface, where these secrets are typically exposed. The platform treats this risk as a critical Non-Human Identity (NHI) Exposure.
External Discovery
ThreatNG's External Discovery is foundational, as leaked credentials often reside outside the corporate network, making them invisible to internal tools. ThreatNG performs this discovery solely externally, without using connectors.
How it helps: The core of the risk is finding the exposed secret. ThreatNG uses its discovery mechanisms to map every subdomain and technology stack, including those categorized as Development & DevOps (e.g., GitHub, Bitbucket) and Mobile App Exposure. This comprehensive mapping ensures that the repositories and mobile applications where secrets are mistakenly committed are brought into scope for assessment.
Example of ThreatNG helping: ThreatNG discovers the organization's corporate mobile application in an online marketplace. This discovery initiates an internal check of the app's code for hardcoded credentials, a known source of leaked agent keys.
External Assessment
ThreatNG’s assessment modules directly target the different exposure vectors for AI agent credentials and quantify the severity of the leak.
Highlight and Examples:
Direct Credential Exposure: The Non-Human Identity (NHI) Exposure Security Rating (A–F scale) is a critical governance metric that quantifies this threat, specifically targeting high-privilege machine identities.
Example: The Sensitive Code Discovery and Exposure capability scans public code repositories for various Access Credentials and Configuration Files. ThreatNG flags the exposure of a Heroku API Key, Google Cloud Platform OAuth Access Token, or Slack Token, which AI agents commonly use to perform actions. This finding converts a chaotic technical detail into irrefutable evidence with Legal-Grade Attribution.
Mobile App Exposure: The Mobile App Exposure assessment evaluates apps found in marketplaces for hardcoded secrets.
Example: ThreatNG discovers an organizational mobile app and analyzes its contents, flagging the presence of a hardcoded Amazon AWS Access Key ID or GitHub Access Token. This is a direct risk, as these keys could be used by an attacker to access the AI agent's underlying cloud resources, leading to data exfiltration or system tampering.
Continuous Monitoring
ThreatNG provides Continuous Monitoring of the external attack surface and digital risk.
How it helps: Since leaked credentials can be pushed to a repository at any time, continuous monitoring is necessary to minimize the exposure window. If a developer attempts to revoke a leaked key but accidentally pushes it back later, ThreatNG's continuous scanning immediately detects its reappearance, preventing a recurrence of the risk.
Investigation Modules
These modules enable analysts to locate and contextualize the exposed credential within its broader risk framework.
Highlight and Examples:
Online Sharing Exposure: This module identifies an organization's presence on code-sharing platforms such as Pastebin and GitHub Gist.
Example: An analyst uses this module to find a Pastebin snippet of proprietary code that contains an unencrypted database password. This credential likely provides the AI agent with unauthorized access to a critical dataset, confirming the severity of the leak.
External Adversary View and MITRE ATT&CK Mapping: ThreatNG aligns findings with attacker methodologies.
Example: The compromise of an AI agent's service account credentials is automatically mapped to the corresponding MITRE ATT&CK technique, such as an Initial Access method. This helps security leadership prioritize the remediation of the leaked credentials based on its function as a high-value entry point for the adversary.
Intelligence Repositories
The Intelligence Repositories (DarCache) are used to prioritize the risk associated with compromised credentials.
How it helps: The Compromised Credentials (DarCache Rupture) repository provides continuous data on credentials found on the dark web. While AI agent keys are often unique, this repository helps inform the overall picture of compromised identities, which may include NHI Exposure findings. The Vulnerabilities (DarCache Vulnerability) repository also helps prioritize if a leaked credential grants access to a system with an actively exploited KEV vulnerability.
Cooperation with Complementary Solutions
ThreatNG's high-certainty intelligence about a leaked credential is used to automate security responses in other systems.
Cooperation with Secrets Management Platforms: ThreatNG identifies a leaked credential via NHI Exposure.
Example: This external finding is routed to a complementary Secrets Management platform, which triggers an automated workflow to revoke or rotate the exposed key. This action neutralizes the attacker's ability to use the stolen AI agent credential, protecting the AI infrastructure it was designed to access.
Cooperation with Security Orchestration, Automation, and Response (SOAR) Systems: High-priority findings, like a leaked credential, are used to trigger automated remediation.
Example: The finding of a leaked Stripe API Key is passed to a complementary SOAR system, which then automatically creates a high-priority ticket in the internal system and simultaneously contacts the Finance/Security team to confirm and manually revoke the key in the payment gateway.
