Non-Human Identity Exposure for LLM Agents
The Non-Human Identity Exposure for LLM Agents is a particular cybersecurity risk that involves the public disclosure of the automated, machine-level credentials belonging to an LLM application or AI agent that operates on behalf of an organization.
This concept focuses on a security failure in which an identity not tied to a human user is inadvertently made accessible to an attacker from the external environment.
Detailed Breakdown of the Non-Human Identity
In the context of AI, a non-human identity (NHI) is any digital secret used by a program—not a person—to authenticate and perform actions across integrated systems.
Identity Types: These credentials are high-privilege keys that grant programmatic access to critical resources:
API Keys/Tokens: Keys for LLM providers (e.g., to access a fine-tuned model), third-party services (e.g., Stripe, SendGrid), or internal APIs.
Cloud Service Account Credentials: Keys (e.g., AWS Access Key IDs, Azure Service Principal secrets) that allow the LLM agent to interact with cloud resources like data storage or compute instances.
Database/System Connection Strings: Passwords or access keys that allow the agent to read, write, or query internal databases, vector databases, or data lakes.
The Exposure: The "exposure" is the act of these secrets moving from a secure, internal vault to a public, external location where an attacker can harvest them. This typically occurs due to developer error or misconfiguration, resulting in a Leaked AI Agent Credential. Familiar sources of exposure include:
Accidentally committing configuration files containing the secret to a public GitHub repository.
Leaving plain-text credentials in a publicly readable Cloud Bucket or internal development log file.
Hardcoding keys into mobile applications or front-end code that can be easily reverse-engineered.
The Critical Risk (Excessive Agency): The exposure is critical because non-human identities often adhere to the principle of convenience, not least privilege. They may be granted overly broad, persistent permissions to prevent a single system failure, leading to Excessive Agency.
If an NHI credential is leaked, the attacker gains the agent’s full, often excessive, privileges. They can use this access to take control of the LLM agent, command it to exfiltrate data, commit Cloud Bucket Poisoning, or shut down services—all while operating under the guise of a legitimate, authorized machine identity.
Therefore, Non-Human Identity Exposure for LLM Agents is a pivotal security failure that bypasses traditional human-centric defenses (like multi-factor authentication) and provides an attacker with a high-privilege backdoor into the AI system and its downstream dependencies.
ThreatNG provides a robust defense against Non-Human Identity Exposure for LLM Agents by treating this risk as a core external credential and access failure. It uses its purely external, unauthenticated discovery to find the exposed keys and tokens before an attacker can weaponize the agent's identity.
External Discovery
ThreatNG’s External Discovery is crucial for locating the precise source of the credential leak, which is often external to the primary network and therefore invisible to traditional internal security tools.
How it helps: The core of the risk lies in finding the exposed secret. ThreatNG’s discovery mechanisms continuously scan for digital footprints in high-risk areas. This includes mapping all domains and subdomains to identify associated Development & DevOps tools and services where secrets are often mistakenly stored.
Example of ThreatNG helping: ThreatNG identifies a public-facing endpoint running a service categorized under AI Model & Platform Providers. This discovery initiates a targeted check, leading to the inspection of public code linked to that asset, where a hardcoded service account ID might be found.
External Assessment
ThreatNG’s assessment modules are specifically designed to quantify the severity of a leaked non-human identity.
Highlight and Examples:
Direct Credential Exposure Rating: The Non-Human Identity (NHI) Exposure Security Rating (A–F scale) is the primary metric for this risk. It assesses the organization's overall vulnerability to threats originating from leaked machine identities.
Example: The Sensitive Code Discovery and Exposure capability scans public repositories (like GitHub) and forums for various Access Credentials and Configuration Files. If ThreatNG finds a leaked AWS Access Key ID associated with an LLM agent’s service account, it immediately flags a critical NHI Exposure risk. This finding provides Legal-Grade Attribution that the identity is compromised, allowing an attacker to operate with the agent's privileges.
Mobile App Exposure Assessment: This assessment targets credentials hidden within application code.
Example: ThreatNG discovers the organization's mobile application and analyzes its compiled code for hardcoded secrets, flagging a Twilio API Key used by the LLM agent to send notifications. This confirmed leak exposes a non-human identity, enabling an attacker to hijack the agent's messaging capabilities.
Continuous Monitoring
Continuous Monitoring of the external attack surface ensures that if a compromised credential is replaced but later accidentally reposted (known as credential re-exposure), the security team is alerted instantly.
How it helps: Since leaked NHI credentials can surface at any time, often due to automated processes or multiple developers working on the same code, continuous monitoring tracks public code and file-sharing sites. This minimizes the exposure window during which an attacker can use the Leaked AI Agent Credential to perform malicious actions.
Investigation Modules
These modules provide the context needed to prove the exploitability of the leaked identity and accelerate response.
Highlight and Examples:
Online Sharing Exposure: This module identifies an organization's presence on public forums and code-sharing sites such as Pastebin and GitHub Gist.
Example: An analyst uses this module and finds a developer's post on a public forum that includes a plaintext PostgreSQL connection string for the vector database, along with the agent's specific user ID. This confirms the direct exposure of the non-human identity and provides the exact target information needed for immediate remediation.
External Adversary View and MITRE ATT&CK Mapping: ThreatNG automatically correlates the exposed identity with attacker techniques.
Example: The discovery of a leaked service account credential is automatically mapped to a MITRE ATT&CK technique, often related to Initial Access or Credential Access. This helps security leadership prioritize the immediate revocation of the compromised identity given its role as a high-value attack vector.
Intelligence Repositories
ThreatNG’s Intelligence Repositories (DarCache) provide crucial context for prioritizing the remediation of the identity exposure.
How it helps: The Vulnerabilities (DarCache Vulnerability) repository integrates KEV (Known Exploited Vulnerabilities) data. If the system that the leaked credentials can access has a known, actively exploited vulnerability, the risk of the Non-Human Identity Exposure is significantly elevated. This allows the organization to focus on revoking keys that grant access to the most vulnerable parts of the infrastructure first.
Cooperation with Complementary Solutions
ThreatNG's high-fidelity detection of leaked NHI credentials automates identity protection workflows across the enterprise.
Cooperation with Secrets Management Platforms/IAM: ThreatNG provides external, definitive proof of the compromised identity.
Example: When ThreatNG identifies a leaked AWS Access Key ID belonging to an LLM agent, this external finding is routed to a complementary Secrets Management Platform or Identity and Access Management (IAM) tool. The IAM system automatically executes an emergency revocation and key rotation playbook for that specific non-human identity, immediately neutralizing the attacker's access.
Cooperation with SOAR (Security Orchestration, Automation, and Response) Systems: The high-priority finding triggers an automated response.
Example: The alert regarding the leaked credential is sent to a complementary SOAR system, which automatically creates a high-priority ticket, isolates the affected code repository to prevent further leaks, and initiates a forensic audit on the linked cloud resources to check for evidence of compromise by the stolen identity.
