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Find out whether attackers can manipulate your AI applications to access sensitive data, bypass safeguards, or misuse automated workflows.
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AI applications introduce new attack surfaces across prompts, models, data pipelines, and automated workflows.
These systems process untrusted inputs, interact with internal data, and execute actions across multiple services. Attackers exploit those connections to manipulate models, expose sensitive information, or trigger unsafe behavior.
Most security testing was never designed to evaluate these risks.
Prompt injection that manipulates model behavior
Retrieval systems exposing internal knowledge
Model outputs leaking sensitive data
Agents executing actions beyond intended permissions
System prompts or policies exposed through model responses
Model APIs revealing internal logic or training signals
AI copilots connected to internal knowledge
Assistants retrieving internal documents, tickets, or databases through RAG pipelines.
Customer-facing AI interfaces
Chatbots, AI search, or support assistants exposed to external users.
LLM-powered product features
Applications where models process user inputs or generate outputs inside production workflows.
AI agents executing actions
Agents interacting with internal APIs, automation tools, or operational systems.
AI systems connected to sensitive data
Models retrieving or analyzing proprietary documents, code, financial data, or customer records.
we45 simulates how attackers manipulate AI-powered applications across prompts, models, retrieval pipelines, APIs, and automated workflows.
This assessment shows where these systems break by exposing sensitive data, bypassing safeguards, or triggering unintended actions in production.
we45’s AI-Native Application Penetration Testing exposes real exploitation paths that can leak sensitive data, bypass safeguards, or trigger unintended actions inside production AI systems.
What we test
AI models interpret language rather than fixed commands. This makes it possible for carefully written prompts to influence responses or push the system beyond its intended boundaries.
Many AI systems retrieve answers from internal documents, databases, or knowledge sources. Under certain conditions, those same mechanisms can reveal information that should remain private.
Safety rules and filters are meant to block harmful prompts or responses. Attackers often work around these controls indirectly, using prompts designed to slip past them.
Some AI systems can trigger actions through connected tools, APIs, or automation platforms. When those systems receive manipulated inputs, they may perform actions nobody intended.
AI features are increasingly embedded in business processes like support, search, and operational workflows. Manipulated outputs can influence decisions or trigger actions based on misleading information.
AI applications rarely operate alone. Connections to external models, plugins, and third-party services introduce additional trust relationships that attackers may exploit.
Every assessment begins with mapping the AI environment, identifying models, APIs, retrieval pipelines, and integrations to understand how the system operates and where sensitive data flows or manipulation may occur.
Potential attack paths are analyzed across prompts, model interactions, and system connections to identify where security risks exist and which scenarios are most likely to be exploited.
The system is challenged using crafted inputs and interaction patterns designed to influence behavior, bypass safeguards, and expose how it responds under deliberate attack.
Each issue is documented with clear evidence and context, providing teams with prioritized actions to address the most critical risks and reduce exposure quickly.
Retrieval pipelines are evaluated for prompt injection, knowledge base manipulation, and unintended data exposure across documents and tenants.
Multi-agent workflows are examined for unsafe actions, permission overreach, and the ability to trigger operations beyond intended boundaries.
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Tokens, API keys, and integration permissions are reviewed for overexposure, reuse, and paths that could enable lateral movement.
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AI-powered applications are analyzed for business logic abuse, access control gaps, and the risk of sensitive data being exposed through model behavior.
AI systems create complex interactions between models, data sources, and workflows that are not obvious during design. These interactions can form indirect paths to sensitive data and critical functionality.
The behavior of AI systems often changes depending on how inputs are framed and what data is retrieved. Under adversarial conditions, this can lead to unintended exposure of internal or confidential information.
Safeguards and policies may appear effective under normal use but behave differently when deliberately challenged. Certain inputs and interaction patterns can bypass controls without triggering obvious failures.
AI systems connected to tools and workflows can influence real operations. When manipulated, these systems may initiate actions or decisions that fall outside their intended scope.
Not all findings carry the same weight or urgency. The assessment distinguishes between theoretical issues and those that present immediate real-world risk.
AI-related risks are often difficult to explain without concrete examples. The findings provide clear and defensible evidence that connects system behavior to business impact.
we45 contributes to the global security community through research, training, and hands-on security engineering.
Our team trains security professionals at major conferences including Black Hat and RSA Conference, and SecurityReview.ai was recently recognized with the SANS Difference Maker Award for advancing modern security practices.
Threat models delivered across modern software architectures
Secure product launches supported by we45
Of vulnerabilities discovered across applications, APIs, and cloud environments
Trained through AppSecEngineer
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