Security testing for AI-powered apps and agents

What is Security of AI applications and systems?

Applications that incorporate artificial intelligence introduce new attack surfaces that traditional pentesting does not cover: prompt injection (direct and indirect), data exfiltration through the context of the model, abuse of tools and actions that the agent can execute, supply chain of AI models and dependencies, and emerging behavior not foreseen in the design. This service is a technical security assessment: we perform threat modeling and authoritative testing on applications, agents and pipelines that use language models or other AI systems.

The process starts with threat modeling: we identify assets (data, actions, reputation), threat actors (malicious users, poisoned content, compromised vendors), trust boundaries (which component trusts which), data flows (what goes into the model, what goes out, what gets stored), and abuse scenarios relevant to the specific application context. We do not apply a generic checklist: the threat model is adapted to the architecture, use case and risk profile of the system evaluated.

The authorized tests cover the specific AI vectors: direct prompt injection (the user tries to manipulate instructions) and indirect (external content that the system consumes tries to alter behavior), exfiltration of data from the context (system prompt, data from other users, privileged information accessible to the model), scaling of actions in agents (the agent executes tools with permissions that the user should not have directly), abuse of tools and APIs (manipulated parameters, rate limit, irreversible actions without confirmation), supply chain of models (dependencies, artifacts, provenance, trojanized models) and bypass of controls (content filters, guardrails, detection systems).

We also review the traditional security of the application that hosts the AI: authentication, authorization, secret management, classic injection (SQL, command), XSS, SSRF, deserialization, logging, session management, and infrastructure configuration. Classic vulnerabilities are still relevant and can be amplified when a model has access to internal resources.

Each finding is documented with: technical description, reproducible evidence (steps, payloads, responses), contextualized severity, potential impact, root cause, and prioritized remediation recommendation. We do not deliver generic lists of OWASP Top 10 for LLMs without context: each finding is specific to the evaluated and verifiable system.

The scope is defined before acting: which systems are tested, with which accounts, in which windows, with what test data, with what limits (do not modify actual production, do not exfiltrate real data beyond evidence, do not affect availability). Testing is authorized: there is formal agreement, defined environment, and communication during execution.

No security test guarantees total absence of vulnerabilities. We provide evidence of the current status within the agreed scope; Residual risks (out-of-scope, future, context-dependent) are explicitly noted. This is not a certification: it is a specific technical assessment. For ongoing risk program you need iteration, scaling up, and possibly AI governance consulting.

This service does NOT replace AI Governance: that scope (organizational operating model, policies, roles, case catalog, approval, and governance controls) is governance consulting, not the technical pentesting/assessment of this subservice. Both can complement a risky program, but they respond to different search intentions and purchasing needs. The assessment provides technical evidence; The government defines how the organization decides and controls.

Optionally, we offer retest after remediation to verify that the findings have been effectively corrected, and a technical session with the development team to delve into causes and prevention.