AI Agent Security
AI agents are no longer a research project — they're in production, holding API keys, reading customer data, and taking actions on your behalf. That convenience is exactly what makes them a new, largely untested attack surface.
Why AI Agent Security Can't Wait
By 2026, a large share of enterprises report having more than 100 agents deployed across their stack, and a majority have already experienced a confirmed or suspected AI agent security incident in the past year. The gap isn't awareness — most security leaders say they're confident in their existing policies. The gap is testing: monitoring coverage, accountability structures, and pre-deployment controls for agents have barely kept pace with how fast they've been adopted.
Traditional application security tooling wasn't built for this. A web app has a fixed set of routes and inputs; an AI agent has a natural-language interface that can be manipulated into ignoring its own instructions, calling tools it shouldn't, or leaking data it was never meant to expose.
What We Test
Alastor InfoSec's AI Agent Security engagements are built around how agents actually fail in the wild, not generic checklists:
- Prompt injection — direct and indirect injection through user input, retrieved documents, or tool outputs that hijacks an agent's behavior.
- Tool and function-call abuse — forcing an agent to call sensitive tools (payments, data export, admin actions) outside its intended scope.
- Excessive agency — agents granted more permissions than their task requires, and what an attacker can do with that surplus.
- Data exfiltration paths — chained prompts or tool calls that quietly move sensitive data out through logs, responses, or third-party integrations.
- Memory and context poisoning — attacks that corrupt an agent's long-term memory or shared context so future sessions behave maliciously.
How It Fits Into Your Stack
AI Agent Security engagements run alongside our MCP Security testing, since almost every production agent today is wired up to tools and data sources through the Model Context Protocol. We test the agent's reasoning layer and its tool-calling layer together, because in practice that's where real incidents originate — an agent doesn't get compromised in isolation, it gets compromised through what it's connected to.
Findings are delivered through the same continuous dashboard as the rest of your Alastor InfoSec coverage, with severity ratings your engineering team can act on immediately rather than a one-time PDF that's stale by the next model update.
Who Needs This
If you've shipped a customer-facing chatbot with tool access, an internal agent that touches production systems, or a workflow automation built on an LLM, you have an AI attack surface today — whether or not it's been tested. This is especially urgent for teams handling regulated data, since a compromised agent that mishandles personal data is a DPDP Act (DPDPA) incident, not just a security bug.
Talk to our team about scoping an AI Agent Security assessment for what you've actually shipped, not a generic LLM benchmark.