Prove your AI agents stay inside policy under attack.
Ultra13 combines adversarial agent testing, runtime context enforcement, and post-control validation so security teams can govern AI agents without blocking product teams.
The attack surface is the workflow.
Agent risk does not live in the prompt. It lives in the reasoning, memory, tools, identity, RAG, MCP, and inter-agent communication that make the agent useful in the first place.
the plan-act loop itself can be steered
stored context becomes standing instruction
function calls with real side effects
privileged credentials the agent can borrow
retrieved chunks that read as instruction
external servers and their tool schemas
messages passed between cooperating agents
Four questions Ultra13 answers.
A security review of an agent is not a checklist of model settings. It is a clear answer to what the agent can reach, what can steer it, what it can do, and whether the controls hold.
What can this agent access?
Every identity, credential, tool, data store, and retrieval source the agent can reach — mapped, not assumed.
What can manipulate it?
Every untrusted input path: users, documents, web pages, email, RAG, memory, MCP responses, and other agents.
What can it do?
The full blast radius of its tools and actions — exports, writes, payments, code execution, and downstream calls.
What happens when hostile context enters the loop?
We inject it. Poisoned memory, malicious tools, and indirect injection are replayed against the live agent.
Can we prove the controls work?
Before/after exploit replay and blocked-action logs that show each path is closed — evidence an auditor can read.
Attack coverage.
We run a transparent catalogue of agentic attack families against the live agent loop — hostile users, poisoned context, malicious tools, and real exploit chains.
Enforceable runtime controls.
Findings are only half the job. Each one maps to a control that holds at runtime — enforced at context boundaries, not asked for in a system prompt.
Context provenance
Every span tagged with where it came from — user, document, web, RAG, memory, MCP, tool output, or another agent.
Source/sink enforcement
Decide which context classes may influence which actions. Web content can answer; it cannot authorize an export.
Tool authorization
Inspect tool name, arguments, target resource, identity, and tenant before anything executes.
Memory quarantine
Isolate untrusted memory so poisoned context never becomes standing instruction or implicit authorization.
Sensitive-data redaction
Strip secrets and regulated data before they reach a model, a log, or an outbound call.
Approval gates
Require human approval for high-blast-radius actions, with the real action shown — not a paraphrase.
Action logs
Every decision — allow, block, redact, quarantine, approve — captured as a replayable record.
Tenant isolation
Enforce boundaries so one tenant's context or memory can never reach another's reasoning or tools.
Audit-ready evidence.
Every engagement produces an evidence pack a security team can hand to a board, an auditor, or a customer — no translation required.
The same attack run against the agent before and after controls, side by side.
Each finding rated by impact and likelihood, mapped to the surface it abuses.
The exact source-to-sink and tool-authorization changes that closed each path.
Machine records of every attempt the runtime stopped, with full call context.
What remains open, why, and the compensating control or accepted-risk decision.
Pass/fail per attack family after controls, so re-tests are unambiguous.
A one-page account a board, auditor, or customer can read without translation.
agent uses privileged identity for attacker goal
cross-identity action denied
Each finding ships with the same artifact: the exploit succeeding before the control, the action contained after it, and the runtime decision that closed the path — replayable on demand.
Prompt injection can lead to sensitive information disclosure, unauthorized function access, arbitrary command execution in connected systems, and manipulation of critical decisions. RAG and fine-tuning reduce the surface but do not fully mitigate it — which is why enforcement belongs at runtime, not in the model.
Turn agentic AI risk into evidence your board, auditors, and customers can understand.
We attack one high-risk agent workflow, enforce the controls at runtime, and prove the exploit path is closed.