Prompt Injection
Can an attacker manipulate the agent through direct or indirect instructions?
AI Agent Security
Your AI agent has access. We find out what happens when that access is abused.
ModelStrike red-teams AI agents, tools, permissions, prompts, and integrations to uncover security failures before attackers—or autonomous systems themselves—turn them into incidents.
For teams shipping AI agents, copilots, and agentic workflows into production.
Attack surface mapscope: agent runtime
Diagram: an AI agent inside its runtime trust boundary, connected to tools, APIs, files, a browser, an MCP server, and a database. ModelStrike tests attack paths such as untrusted content steering the agent into misusing an MCP server or reaching a database.
The problem
AI agents create a new attack surface.
Traditional application security still matters. But agentic systems add security boundaries that conventional testing was never designed to reach, because the model itself now decides what happens next.
An AI agent can:
- Interpret untrusted instructions
- Invoke tools
- Call APIs
- Access sensitive information
- Retrieve external context
- Make autonomous decisions
- Chain multiple actions together
- …often in a single request.
Where agentic systems fail
Tool Abuse
Can the agent invoke legitimate tools in unintended or dangerous ways?
Excessive Agency
Can the system take actions beyond what the user or developer intended?
Data Exfiltration
Can sensitive context, credentials, documents, or retrieved information escape?
Authorization Failures
Can the agent cross boundaries between users, tenants, resources, or privilege levels?
Agent Chaining
Can individually safe actions be combined into a dangerous multi-step sequence?
These are common starting points, not a complete list. Every assessment is shaped by your architecture.
What we test
We attack the whole agent, not just the prompt.
Prompt injection is only the entry point. Real impact comes from what the agent can reach afterwards, so ModelStrike tests every boundary between the user, the model, the agent runtime, and the systems it connects to.
- User
- Prompt / Context
- Model
Agent Runtimeplanning · memory · tool calls · approvals
- Tools
- MCP Servers
- APIs
- Browser
- Files
- Databases
- External Services
Instructions & context
- Prompts and system instructions
- Retrieval pipelines
- Agent memory
- Multi-agent communication
- Data handling
Tools & integrations
- Tool schemas
- Function calling
- MCP implementations
- API integrations
- Browser and computer control
Access & control
- Authorization
- Authentication boundaries
- Secrets exposure
- Human approval boundaries
- Dangerous action chains
Services
Expert-led AI agent security engagements.
Every engagement is scoped to your architecture, risk profile, and timeline.
AI Agent Security Assessment
Manual adversarial review of an agentic application and its surrounding architecture.
Includes
- Threat modeling
- Attack surface mapping
- Adversarial testing
- Tool and permission review
- Exploit validation
- Remediation guidance
AI Agent Red Team
A deeper offensive engagement focused on discovering realistic attack chains across the AI system.
Includes
- Prompt injection
- Indirect prompt injection
- Privilege escalation
- Tool abuse
- Data extraction
- Cross-system attack chains
- Autonomous failure scenarios
Pre-Deployment Security Review
For teams preparing to launch an AI feature or complete an enterprise security review.
Includes
- Architecture review
- Production-risk analysis
- Guardrail validation
- Authorization review
- Deployment recommendations
- Prioritized remediation report
Pricing depends on scope. Tell us about your system and we'll propose an engagement.
Talk to ModelStrikeMethodology
How an assessment works
A structured process built to produce reproducible findings, not a pile of theoretical concerns. Testing scope, environments, and authorization are agreed in writing before any testing begins.
01
Map
Understand the architecture, permissions, data, tools, trust boundaries, and business-critical actions.
02
Attack
Test the system adversarially across prompts, tools, integrations, authorization, data access, and multi-step workflows.
03
Validate
Separate theoretical concerns from reproducible security failures and document the attack path.
04
Fix
Provide prioritized findings, remediation recommendations, and retesting guidance.
Illustrative examples
Examples of the types of issues an assessment may uncover
These are representative categories of agentic security failures, described generically. They are not findings from, or references to, any specific organization.
Example issue typesNot customer data
- Prompt injectionIndirect prompt injection causing unauthorized tool execution
- AuthorizationAgent accessing resources outside the intended user's authorization boundary
- Data exposureSensitive information leaking through retrieved context
- MCPOverly permissive MCP server exposing dangerous capabilities
- Approval controlsConfirmation controls bypassed through multi-step agent behavior
- Untrusted inputAttacker-controlled content influencing downstream agent decisions
- SecretsCredentials or secrets becoming accessible to model context
- Action chainingChained low-risk actions producing a high-impact outcome
Why ModelStrike
AI security requires thinking like both an attacker and an AI engineer.
Conventional penetration tests often stop at the API. Generic AI evaluations often stop at the model. The most serious agent failures sit between the two — where model behavior meets real permissions.
ModelStrike focuses on that intersection. We are building toward turning repeatable testing techniques into automated AI-security tooling, but today every engagement is led by hands-on experts.
Offensive security
Adversarial thinking, exploit validation, and an attacker's view of what is actually reachable.
AI engineering
How models, prompts, retrieval, memory, and tool calling behave in real production stacks.
Autonomous agents
Planning loops, multi-step execution, delegation between agents, and where autonomy creates new failure modes.
Application security
Authentication, authorization, tenancy, secrets, and API security — the foundations agents inherit.
Who this is for
Built for teams putting agents in front of real systems.
Typically B2B software companies with an AI agent, copilot, or agentic workflow in production — or about to be — and buyers who need confidence before rollout.
You're deploying an AI agent
Your product can act — not just answer questions.
Your agent touches sensitive systems
It can reach customer data, APIs, internal documents, browsers, databases, or infrastructure.
Enterprise customers are asking security questions
You need stronger answers about how your AI system behaves adversarially.
You're giving an LLM more autonomy
Permissions and attack impact increase as capabilities expand.
Security philosophy
Least privilege for machines that can think in actions.
No architecture is perfectly secure, and no model is perfectly obedient. Good agent security assumes the model can be wrong or manipulated, and limits what that can cost you.
Minimize permissions
Grant each agent and tool only the access the task requires.
Isolate trust boundaries
Separate users, tenants, tools, and data so one failure stays local.
Assume external context can be hostile
Web pages, documents, emails, and tool output can all carry instructions.
Validate actions, not intent
Enforce policy on what the agent does, rather than trusting what the model meant.
Require approval for high-impact actions
Put a human in the loop where the consequences are hard to reverse.
Log security-relevant activity
Record tool calls, data access, and decisions so incidents can be investigated.
Design failures to be contained
Plan for the agent being wrong or manipulated, and limit the blast radius.
Before you give an AI agent more access, find out how that access can fail.
ModelStrike can evaluate your agent architecture, attack surface, and production security controls.