AI Agent Attack Surface
The AI Agent Attack Surface, in the context of cybersecurity, refers to the entire collective exposure of an organization's autonomous Artificial Intelligence (AI) systems, particularly those that use Generative AI models and operate with a degree of independence (known as "agents" or "agentic AI").
This attack surface encompasses every point where an attacker can interact with, observe, manipulate, or compromise the agent, its data, or its underlying infrastructure.
Four primary components define it:
The Interface/Input Layer: This is the most direct point of attack. It includes user-facing natural language interfaces (such as chatbots or copilots), APIs, or any communication channel through which a user or another system provides input to the AI agent. This is where attacks like Prompt Injection occur, aiming to hijack the agent's intent or make it reveal sensitive data.
The AI Model Core (The Brain): This includes the underlying Large Language Model (LLM) or other foundation models the agent relies on. Attacks here target the integrity or confidentiality of the model itself. Risks include Model Stealing (extracting proprietary model weights), Data Poisoning (corrupting training data), or exploiting vulnerabilities within the model architecture to cause unintended or harmful behavior.
The Retrieval/Knowledge Layer (The Memory): Modern agents often use Retrieval-Augmented Generation (RAG) to access external or internal knowledge bases, such as vector databases or internal company documents. The attack surface here includes the databases, ingestion pipelines, and retrieval logic. An attacker may aim to cause data leakage by manipulating the agent's prompts to retrieve confidential documents or by injecting malicious data into the knowledge base itself.
The Operational/Tooling Layer (The Hands and Feet): This is perhaps the most dangerous component. AI agents are defined by their ability to use external tools or take actions (e.g., executing code, sending emails, running SQL queries, making external API calls). The attack surface here is the set of all permissions and integrations the agent holds. An attacker who successfully compromises the agent's logic can force it to execute unauthorized, harmful actions using its legitimate permissions, leading to lateral movement or system compromise.
The AI Agent Attack Surface is significantly broader and more complex than traditional application security, as it involves non-deterministic, multi-step execution and combines code vulnerabilities with logical and ethical exploits.
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 emerging risks of the External AI Attack Surface without requiring any internal access or credentials. It approaches the problem exclusively from the perspective of an unauthenticated attacker.
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 AI assets, especially those running outside of traditional IT oversight (Shadow AI).
Subdomain Intelligence: ThreatNG finds all associated subdomains, which can reveal exposed API endpoints or development environments hosting AI models. This process identifies the hosting environment, including subdomains hosted on major cloud platforms like AWS, Microsoft Azure, and Google Cloud Platform, as well as PaaS solutions.
Technology Stack Identification: ThreatNG provides exhaustive, unauthenticated discovery of nearly 4,000 technologies. Crucially, this includes hundreds of technologies categorized under Artificial Intelligence, as well as numerous vendors in AI Model & Platform Providers and AI Development & MLOps. This is how ThreatNG discovers the presence of a Generative AI endpoint or an AI-related service without authentication.
Example of ThreatNG Helping: ThreatNG discovers an unmanaged subdomain, ai-test.yourcompany.com, running a technology identified as a custom AI Development/MLOps tool. This discovery identifies a Shadow AI asset, which the security team was previously unaware of, allowing them to bring it under governance.
External Assessment for AI Risks
ThreatNG's external assessment modules flag the core risks associated with the external AI attack surface:
Data Leak Susceptibility: This rating is derived from identifying external digital risks, such as Cloud Exposure, including exposed open cloud buckets. Misconfigured cloud buckets are shared locations for storing unauthenticated AI training data or model weights.
Non-Human Identity (NHI) Exposure: This critical governance metric quantifies vulnerability to threats originating from high-privilege machine identities, such as leaked API keys and service accounts. These NHIs are often the credentials an AI agent uses to interact with systems, and their exposure can lead to severe compromise.
Cyber Risk Exposure (Sensitive Code): This rating is based on findings that include Sensitive Code Discovery and Exposure (code secret exposure). This uncovers leaked credentials or API keys in public code repositories and mobile apps that an attacker could use to compromise an AI-enabled service directly.
Example of ThreatNG Helping: ThreatNG's Data Leak Susceptibility assessment flags a public-facing AWS S3 bucket containing files tagged "TrainingData-LLM-V2." This unauthenticated, immediate finding reveals a critical data-exposure risk that requires immediate closure to protect proprietary AI assets.
Reporting and Continuous Monitoring
ThreatNG provides Continuous Monitoring across the external attack surface, digital risk, and security ratings for all organizations.
Reporting: ThreatNG provides comprehensive reports, including Executive, Technical, and Prioritized views (High, Medium, Low). This allows security leaders to quickly communicate the business risk of exposed AI assets, such as a high Data Leak Susceptibility rating due to an exposed cloud bucket containing AI data.
Security Ratings: All findings contribute to A-F Security Ratings, converting complex technical risks into an understandable business context.
Investigation Modules
The Investigation Modules allow security teams to drill down and validate AI-related findings:
Domain Intelligence: This module can proactively check the availability of Web3 domains (like .eth and .crypto). This helps secure the brand presence of AI projects in new digital spaces and detect potential risks, such as brand impersonation.
Mobile Application Discovery: This module discovers an organization’s mobile apps in various marketplaces and checks their contents for Access Credentials (including API keys and access tokens) and Security Credentials. If an internal AI agent's credentials were accidentally embedded in a mobile app, this module would expose the leak.
Online Sharing Exposure: This module identifies the presence of organizational entities on online code-sharing platforms such as Pastebin and GitHub Gist. This is a crucial defense against secret exposure, as developers might inadvertently paste LLM API keys or proprietary code snippets into these public forums.
Example of ThreatNG Helping: The Username Exposure module performs a passive reconnaissance scan across social media and high-risk forums. ThreatNG identifies a developer's username on a forum alongside a discussion about a new proprietary AI model, allowing the organization to proactively monitor that developer's digital footprint for leaked access credentials.
Working with Complementary Solutions
ThreatNG's focus on unauthenticated external discovery provides foundational intelligence that can significantly enhance the effectiveness of complementary solutions like Identity and Access Management (IAM) and AI Security Posture Management (ASPM) platforms.
Complementary Solutions (IAM/Secrets Management): ThreatNG's Non-Human Identity Exposure capability identifies leaked API keys and high-privilege credentials on the external attack surface. It provides the irrefutable evidence of the leak. This external intelligence can be instantly fed to an IAM or Secrets Management solution, triggering an automated workflow to revoke or rotate the exposed key. For example, ThreatNG finds a leaked AWS Access Key ID, and the IAM solution automatically revokes the key's permissions, protecting the AI infrastructure it accesses.
Complementary Solutions (ASPM/GRC): ThreatNG performs continuous, outside-in evaluation of an organization's Governance, Risk, and Compliance (GRC) posture. It maps external risks to frameworks like NIST CSF and ISO 27001. This external GRC assessment provides an ASPM with critical context on the compliance risks posed by exposed assets, allowing the ASPM to prioritize internal remediation efforts based on external severity and regulatory mandates. For example, ThreatNG flags an exposed AI vendor API on a subdomain, and the ASPM platform uses this finding to prioritize an internal review of that specific vendor's security controls to maintain ISO 27001 compliance.
