Agents Take Instructions. Not All of Them Come from You.
SecureITX red-teams AI agents against the OWASP LLM Top 10 and MITRE ATLAS. Every finding is validated by exploitation. Runtime controls enforce the fix inside the execution path.
- 10
- OWASP LLM risk categories tested
- MITRE
- ATLAS technique on every finding
- 100%
- findings validated by exploitation
Start with the ground truth
Agents have an attack surface traditional software does not.
A web application validates form fields. An AI agent accepts natural language instructions, reasons across multiple steps, calls tools with real side-effects, and delegates to other agents. That is a wider and less bounded attack surface by design. Three threat surfaces dominate.
Agents accept natural language from users, tool responses, and messages from peer agents. Every one of those channels is a live injection point. At the model layer, a precisely crafted malicious instruction is indistinguishable from a legitimate one.
The risk: one well-placed directive can redirect an agent toward attacker-controlled goals without raising an error or generating a log entry.
Agents call tools with real-world consequences: database writes, file operations, API calls, credential access, outbound email. A compromised agent does not observe. It acts, often before anyone is notified.
The risk: excessive agency is the most common finding in our red-team engagements. Agents are routinely granted broader tool permissions than the task they perform requires.
Our AI Governance practice puts an enforced control plane over every tool callAgents delegate to sub-agents and ingest results from external tools and models. Every handoff carries an implicit trust grant the receiving agent cannot independently verify. One compromised link in a multi-agent chain contaminates everything downstream of it.
The risk: agent-to-agent prompt injection and third-party supply-chain compromise both appear in the OWASP LLM Top 10 because multi-agent systems multiply the blast radius of any single failure.
The threat landscape
The OWASP LLM Top 10. All ten. Fully covered.
OWASP published the LLM Application Top 10 because AI agents fail in ways that existing web and API security frameworks do not fully address. SecureITX tests and hardens against every category, mapped to MITRE ATLAS techniques and your specific stack.
Each category has a concrete defence. Below is how SecureITX finds the exposures, fixes the architecture, and monitors what is left running.
Agent red team
We attack your agents the way real adversaries do.
Every engagement starts from the OWASP LLM Top 10 and tags each finding with a MITRE ATLAS technique. We test input channels, tool authorization boundaries, memory boundaries, and multi-agent trust chains. We do not produce theoretical risk scores. We produce exploitation evidence.
- Direct and indirect prompt injection across every channel the agent accepts.
- Tool boundary testing: what the agent can actually reach versus what it was granted.
- Multi-agent trust chain attacks, including agent-to-agent goal hijack.
- Every finding is validated by exploitation before it reaches the report.
Select a category to see the attack pattern and SecureITX defence.
Runtime hardening
Controls enforced at execution, not documented in policy.
Policy documents do not stop a prompt injection. Runtime controls do. SecureITX deploys a hardening layer directly in the agent execution path: input classification, capability-based tool authorization, output sanitization, and session isolation. Every control enforces before any action reaches a downstream system.
- Input classification flags injection attempts before they reach the model.
- Capability-based authorization: each agent gets only the tools its task requires.
- Output sanitization removes executable content before any downstream system receives it.
- Session isolation prevents cross-session data bleed between users and agents.
Controls deployed in the execution path, not bolted on the side.
Continuous monitoring
Behavioral drift detected before it becomes an incident.
Red-team testing reveals the weaknesses present at deployment. Behavioral monitoring catches the compromises that happen afterward. SecureITX profiles each agent at baseline and watches continuously for deviation: unusual tool call patterns, credential access outside normal windows, and outputs inconsistent with established behavior.
Anomalies route to a human instead of silently executing.
Built to the standards
Four frameworks. One integrated practice.
Agent AI security does not yet have a single authoritative standard. SecureITX covers all four frameworks that regulators, insurers, and enterprise risk teams currently reference, so findings translate directly into the language your governance team speaks.
11:43:58 agent#data-analyst excessive tool grant LLM08 ATLAS:AML.T0049 remediated
11:43:51 agent#customer-svc context bleed across sessions LLM06 ATLAS:AML.T0056 ✓ fixed
11:43:44 agent#classifier plugin overprivilege LLM07 revoked ✓ hardened
Part of our AI security stack
Three layers. One complete posture.
Governance without hardening, MCP without red-teaming, agents without policy: each gap is common. Each is fixable. All three are addressed here.
Why SecureITX
Agent security is a discipline, not a scanner.
Seventeen years in adversarial security means we test agents with the same rigor we bring to network penetration and application testing. We build the controls we recommend, run them in our own production systems, and deliver exploitation-validated findings.
Find out what your agents do under adversarial conditions.
The question is not whether your agents work correctly. The question is what they do when someone is actively trying to make them work incorrectly.
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