AI & Agentic Security

Any organization deploying AI agents with access to production systems, user data, financial operations, or external services. This includes AI-native companies building products on language models, blockchain protocols integrating AI agents for trading or governance, enterprises deploying MCP servers or agentic workflows, platforms built with AI code generation tools, and anyone implementing agentic payment protocols like x402 or AP2. If your AI can take an action that costs money, exposes data, or affects users — it needs adversarial testing by people who know how these systems break.

Traditional application security tests deterministic systems — given the same input, you expect the same output. AI systems are probabilistic, context-dependent, and capable of autonomous decision-making. The attack surface includes prompt injection, memory poisoning, tool-use hijacking, context manipulation, and adversarial inputs that exploit the model’s reasoning rather than its code. A compromised traditional application returns wrong data. A compromised AI agent takes wrong actions — and some actions cannot be undone.

Agentic commerce refers to autonomous AI agents conducting transactions — paying for API calls, purchasing data, procuring services, and settling payments without human intervention. Protocols like x402, Google’s AP2, and Stripe/OpenAI’s ACP are building the payment rails for this economy. Security assessment covers the full transaction lifecycle: payment authorization, signature verification, settlement integrity, receipt validation, and the trust boundaries between agents, users, and service providers. When software pays for software at machine speed, the blast radius of a security failure is measured in seconds, not hours.

The Model Context Protocol (MCP) connects AI agents to external tools, databases, and services — giving agents the ability to read data, execute commands, and modify production systems. MCP servers have become a primary attack surface for agentic AI: tool poisoning, cross-tool hijacking, privilege escalation, shadow server proliferation, and supply chain compromise through malicious packages are all active threat categories. OWASP published a dedicated MCP Top 10 identifying risks from model misbinding to covert channel abuse. If your agents connect to anything through MCP, those connections need security review.

Yes. While Hexens built their reputation in blockchain security, our AI security practice serves any organization deploying AI agents, LLM-powered applications, or autonomous systems in high-stakes environments. The offensive methodology — adversarial thinking, independent review, zero assumptions — applies regardless of whether the system manages on-chain assets or enterprise infrastructure.

Three things. First, our engineers are CTF champions and bug bounty leaderboard veterans — they think like attackers because they operate as attackers. Second, every engagement is AI-augmented: senior offensive engineers direct frontier-class models as force multipliers, achieving coverage and depth that neither humans alone nor automated tools alone can match. Third, we come from blockchain security — an environment where a single missed vulnerability means irreversible financial loss. That standard of rigor carries into everything we do.

Prompt injection is an attack where adversarial input overrides an AI system’s instructions, causing it to execute unintended actions. It has escalated from a theoretical concern to an actively weaponized attack vector. In production systems, prompt injection can cause agents to exfiltrate sensitive data, authorize unauthorized transactions, execute arbitrary code through tool calls, or bypass safety controls entirely. Hexens test for both direct injection (from user inputs) and indirect injection (from external data sources, documents, emails, or tool outputs that the agent processes as trusted context).

Automated scanners run predefined checks against known vulnerability patterns. AI-augmented security means a senior engineer directing a frontier-class model to reason about your system’s specific architecture, identify non-obvious attack paths, and explore edge cases that no scanner has rules for. The model extends the engineer’s reach — analyzing more code paths, generating more adversarial inputs, and reasoning about complex multi-step attack chains — while the engineer provides the judgment, context, and adversarial intuition that the model cannot. The difference is not incremental. It is structural.