AI Agent Drift
AI Agent Drift, in the context of cybersecurity, refers to the unintended deviation of an autonomous AI agent's behavior, decision-making, or operational logic away from its original, intended, and policy-compliant parameters over time.
This phenomenon is a significant security concern because it can transform a trusted, benign agent into a rogue system that introduces risk, violates governance policies, or performs actions that expose the organization to attack.
The drift is typically not a sudden, malicious compromise, but rather a slow, gradual change influenced by several factors:
Interaction and Learning: As the agent interacts with real-world inputs—including prompts, data from connected systems, and feedback loops—it may "learn" new, unintended patterns. This can cause the agent's internal state or parameters to shift, leading to unexpected outputs or tool use.
Environmental Change: Changes in the external environment, such as updates to third-party APIs the agent relies on, evolving user behavior, or shifts in the underlying foundational model (like an LLM update), can break the agent's assumptions and lead to unpredictable actions.
Security Policy Violation: From a security standpoint, drift is critical because it can lead the agent to bypass its original guardrails. A slight, unmonitored behavioral shift might eventually allow an agent to, for example, access data it was never intended to see, execute unauthorized commands, or accidentally leak sensitive information in its responses.
Loss of Attribution: Once an agent drifts significantly, its actions become difficult to trace back to the original, approved policy. This makes auditing, compliance checks, and post-incident forensic analysis extremely challenging, creating a governance void.
Monitoring and detecting AI Agent Drift is essential for maintaining control, ensuring reliability, and preventing agents from becoming a source of internal compromise or compliance failure.
ThreatNG, as an all-in-one External Attack Surface Management (EASM), Digital Risk Protection (DRP), and Security Ratings solution , is uniquely positioned to help organizations address the risks associated with AI Agent Drift without requiring any internal access or credentials. While AI Agent Drift itself is a behavioral phenomenon, ThreatNG focuses on preventing the external exposure that could enable the drift, or detect the external consequences of a drift, by approaching the problem exclusively from the unauthenticated attacker's viewpoint.
External Discovery and Inventory
ThreatNG’s foundational strength is its ability to perform purely external, unauthenticated discovery with no connectors. This is essential for inventorying all assets that an AI agent might potentially interact with, which, if unmanaged, can become a vector for drift or attack.
Subdomain and Technology Identification: ThreatNG provides exhaustive, unauthenticated discovery of technologies across a target’s external attack surface. This includes the discovery of subdomains being hosted on various Cloud Platforms and the identification of nearly 4,000 technologies, including those in the Artificial Intelligence category (265 technologies) , and specific tools used for CI/CD & Source Control, all of which could host or feed an AI agent.
Example of ThreatNG Helping: An AI agent is intended to use an internal document management system, but ThreatNG's Subdomain Intelligence identifies a publicly exposed development instance, staging-agent.yourcompany.com, running an unapproved PaaS service. If the agent drifts to interact with this exposed, unsecured endpoint, it could leak data externally. ThreatNG identifies the risk vector before the drift occurs.
External Assessment for AI Agent Risks
ThreatNG's external assessment modules flag the key exposures that an attacker could leverage to trigger or exploit drift, and the consequences of drift itself:
Non-Human Identity (NHI) Exposure: This critical governance metric quantifies an organization's vulnerability to threats from high-privilege machine identities, such as leaked API keys and service accounts. If an AI agent's credentials are revealed (a security failure that could lead to drift), ThreatNG would detect this external exposure.
Data Leak Susceptibility: This rating is derived from uncovering external digital risks, including Cloud Exposure (specifically exposed open cloud buckets). An AI agent that drifts might access and dump sensitive data into a misconfigured, public cloud bucket, which ThreatNG would immediately detect.
Cyber Risk Exposure (Sensitive Code): This rating is based on findings that include Sensitive Code Discovery and Exposure (code secret exposure). If an AI agent's configuration, which contains security secrets, is accidentally posted to a public repository, ThreatNG would find the external exposure.
Example of ThreatNG Helping: An AI agent's core function relies on an exposed API. ThreatNG’s Data Leak Susceptibility assessment flags a public S3 bucket where the agent was configured to store logs temporarily. An attacker could exploit this exposed bucket to analyze the agent's behavior, leading to manipulative prompt injection that causes drift, or by directly exfiltrating the agent's logs if drift has already occurred and the agent is logging sensitive information.
Reporting and Continuous Monitoring
ThreatNG provides a Continuous Monitoring capability across the external attack surface and digital risk, ensuring that any newly exposed agent component or leaked credential is flagged immediately.
Reporting and MITRE ATT&CK Mapping: ThreatNG automatically translates raw findings into a strategic narrative of adversary behavior by correlating them with specific MITRE ATT&CK techniques. This is vital for AI security, as a finding like "leaked LLM API key" (a sensitive code exposure) would map directly to the initial access phase, allowing security leaders to prioritize threats based on their likelihood of exploitation.
Investigation Modules
The Investigation Modules allow security teams to validate and contextualize AI agent-related exposures:
Sensitive Code Exposure (Code Repository Exposure): This module discovers public code repositories and their contents, including Access Credentials (various API Keys, Access Tokens, Cloud Credentials) and Configuration Files. This is the most direct way to identify an AI agent's core secrets if a developer inadvertently commits them.
Username Exposure: This module conducts a passive reconnaissance scan to determine if usernames associated with AI development, operations, or administrative functions are available or taken across a wide range of social media and forums. This helps mitigate the human element that could facilitate drift or compromise by finding targets for social engineering.
Online Sharing Exposure: This identifies the presence of the organizational entity on online code-sharing platforms such as Pastebin and GitHub Gist. If an employee pastes an LLM prompt or an AI agent's operational script into one of these platforms, ThreatNG discovers the exposure.
Example of ThreatNG Helping: Using the Sensitive Code Exposure module, ThreatNG finds an exposed GitHub repository. Within the repository, ThreatNG identifies an environment configuration file containing a Heroku API Key used by an internal AI agent to provision resources. This provides the security team with the irrefutable evidence required to immediately revoke the key and prevent an attacker from controlling the agent's infrastructure, which could have been a catastrophic consequence of drift.
Intelligence Repositories
ThreatNG’s extensive repositories provide the necessary context to prioritize remediation:
DarCache Vulnerability: This repository integrates intelligence from NVD, KEV, EPSS, and Verified Proof-of-Concept (PoC) Exploits. If an AI agent relies on exposed infrastructure running a technology with a known vulnerability (CVE), ThreatNG provides the necessary data to assess its real-world exploitability, which is vital for prioritizing threats that could lead to system compromise and drift.
Complementary Solutions
ThreatNG's external discovery and risk intelligence can provide critical starting points for complementary solutions like AI Security Posture Management (ASPM) and Continuous Integration/Continuous Delivery (CI/CD) Security Tools.
Complementary Solutions (ASPM/Guardrails): ThreatNG identifies the external exposure of an AI asset. This external finding can be used by an ASPM solution to prioritize its internal work. For example, if ThreatNG flags an exposed Development Environment for a new AI service, the ASPM platform can immediately zoom in on that internal environment, review the agent's internal guardrails, and perform dynamic security testing (like prompt injection) to confirm the integrity of the agent's behavior before it drifts further.
Complementary Solutions (Secrets Management/CI/CD Security): ThreatNG’s detection of a Non-Human Identity exposure, such as a leaked API key or a service account credential, provides the definitive evidence of a breach in the security chain. This external finding can trigger an automated action in a complementary Secrets Management or CI/CD security pipeline tool. The external discovery of the leaked secret initiates the immediate revocation and automated rotation of the credential in the internal system, preventing an external actor from using that key to manipulate the AI agent and cause it to drift from its mission.
