The Attack Surface Nobody Scoped

You deployed a new class of user. It has credentials, tool access, and no judgment.

Why Your Existing Controls Miss It

Your WAF looks for malicious syntax in requests. A prompt injection contains none of it. SAST reads the code your developers wrote and has no view of the instructions an attacker hid inside a support ticket. Your EDR watches endpoints while an agent gets talked into exporting a customer table through an API call it was authorized to make. Every control in your stack assumes an attacker has to break something. An AI system can be persuaded instead, in plain English, by anyone who can reach it.

Meanwhile the AI itself is trusted like infrastructure: connected to your data warehouse, your ticketing system, your CRM, your source control, your customers. Broad access, natural-language control, and no adversarial testing. That combination is the fastest-growing risk we see in client environments.

Attacks arrive as content: emails, PDFs, tickets, web pages, calendar invites
Exploitation requires no code and no CVE
The AI is authorized, so the malicious action looks like normal business logic in your logs
Retrieval layers frequently bypass the permission model your application enforces
Most AI features never went through a security review before launch
Indirect prompt injection: typical chain
01
Plant

An attacker submits a support ticket. Buried in it are instructions addressed to whatever assistant reads the queue.

02
Ingest

Your copilot summarizes the ticket queue. The planted text enters the context window as trusted content.

03
Pivot

The instructions redirect retrieval toward records the requesting user was never authorized to see.

04
Exfiltrate

Results leave through a channel the model already has: a rendered link, an outbound tool call, a drafted reply.

Nothing in that chain needs malware or a privileged position. In your logs it looks like the assistant doing its job.

Where Your AI Exposure Actually Lives

Most organizations have more AI in production than their security team has on record

Public-Facing Agents

Support, sales, and onboarding agents that answer to anyone on the internet and now take real actions: issuing refunds, changing account details, looking up orders, escalating tickets. An unauthenticated attack surface with authenticated reach.

Internal Copilots

Enterprise assistants wired into SharePoint, Google Workspace, Slack, and your wiki. They inherit whatever over-broad permissions those systems accumulated over a decade, then make that data searchable in natural language by every employee.

RAG & Knowledge Bases

Retrieval pipelines that pull from vector stores where document-level access control was an afterthought. If retrieval doesn't enforce the same ACLs as your application, the model becomes a very cooperative data-exfiltration channel.

Tools, Functions & MCP Servers

Every tool you connect is a new privilege grant. We assess function-calling scopes, MCP server trust boundaries, third-party connectors, and whether a compromised or malicious tool can pivot into systems the AI was never meant to touch.

Shadow AI

The AI you don't know about: browser extensions, personal accounts, unsanctioned copilots in the SDLC, and SaaS vendors who quietly enabled AI features on your tenant. We inventory it, classify the data flowing to it, and put a policy around it.

Models & ML Pipelines

Proprietary and fine-tuned models, training data, feature stores, and model registries. Adversarial inputs, model and prompt extraction, membership inference, training-data poisoning, and unsafe deserialization of downloaded model artifacts.

AI Security Services

Engagements scoped to what you've actually deployed, from a single AI feature to an enterprise-wide AI program

AI Attack Surface Discovery

A two-week engagement that answers the question most boards are now asking: where is AI running in our business, what data can it reach, and who can talk to it? Output is an AI asset inventory with a risk-ranked exposure map.

LLM Application Penetration Testing

Full-scope manual testing of an AI-enabled application against the OWASP Top 10 for LLM Applications: direct and indirect prompt injection, jailbreaks, sensitive-information disclosure, insecure output handling, and system-prompt extraction.

Agentic AI Security Assessment

For AI that acts on your systems. Tool-abuse chains, excessive agency, agent identity and authorization, memory poisoning, and multi-agent trust failures. See our agentic AI security services.

RAG & Data-Access Review

We test whether your retrieval layer honors user permissions under adversarial pressure: cross-tenant leakage, ACL bypass through embeddings, poisoned documents, and prompts engineered to surface records the requester should never see.

AI Red Team

Objective-based adversary emulation where AI is the entry point. We chain prompt injection into real business impact: data exfiltration, unauthorized transactions, privilege escalation. Then we measure whether your SOC ever notices. Mapped to MITRE ATLAS.

AI Governance & Readiness

Policy, risk assessment, and control design aligned to the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act, plus the AI security answers your enterprise customers now demand in security questionnaires.

Attacks We Routinely Prove

The findings that show up again and again in real client environments

Indirect Prompt Injection

An attacker never talks to your AI directly. They plant instructions in something the AI will read later: a support email, a resume, a shared document, a product review, a web page your agent browses. The model treats that content as instructions and quietly follows them.

Data Exfiltration Through Rendering

Injected instructions tell the assistant to encode sensitive context into a URL (a markdown image, a link, a tool call) that beacons to attacker infrastructure the moment the response renders. There is no malware to detect and no alert to miss.

Cross-Tenant & Cross-User Leakage

Retrieval that ignores the requesting user's entitlements. We regularly surface other customers' records, unreleased financials, HR files, and internal-only documents through an assistant that was working exactly as designed.

Tool Abuse & Excessive Agency

The agent has a refund tool, a database tool, an email tool. We chain a conversation that gets it to use them in combination the designers never anticipated: issuing credits, changing account ownership, or emailing internal data to an external address.

Identity Confusion

Agents that act with a single service account instead of the requesting user's identity. Every request inherits maximum privilege, authorization checks evaporate, and your audit trail records the agent instead of the human who directed it.

Insecure Output Handling

Model output piped into a browser, a shell, a SQL query, or a downstream API without validation. Classic injection vulnerabilities re-introduced through a component nobody classified as untrusted input, because it's "our own AI."

Our Methodology

Manual, adversarial, and mapped to the frameworks your auditors and customers recognize

1

Discovery & Threat Modeling

We map every model, prompt, retrieval source, tool, connector, and identity in scope, then build a threat model around what the system can do, which is a longer list than what it can say. Trust boundaries are documented before a single test is run.

2

Automated Baseline

Large-scale adversarial prompt suites and fuzzing establish a coverage baseline across jailbreak families, encoding tricks, multi-language bypasses, and known injection patterns. That clears the ground for the manual work that finds the real issues.

3

Manual Adversarial Testing

Senior operators attack the system the way a motivated human would: multi-turn social engineering of the model, context manipulation, injection through every data path it ingests, and abuse of the specific business logic your AI is wired into.

4

Exploitation to Business Impact

A jailbreak transcript on its own proves very little. We chain what we control into consequences you can measure: records exfiltrated, transactions executed, privileges gained. That is what makes the risk legible to an executive.

5

Detection & Guardrail Validation

We test the defenses too: input and output filters, guardrail models, rate limits, human-in-the-loop gates, and whether any of it reached your SIEM. Most clients learn their AI produces no security-relevant telemetry at all.

6

Report, Remediate, Re-Test

Executive narrative, reproducible technical findings with transcripts, and architectural fixes that go deeper than "add a filter." Includes a working session with your engineering team and a free re-test within 90 days.

Frameworks We Align To

So your findings map cleanly to audits, questionnaires, and board reporting

OWASP Top 10 for LLMs

The reference standard for LLM application risk. Every engagement reports findings mapped to it.

MITRE ATLAS

Adversarial tactics and techniques for AI systems: the ATT&CK equivalent for machine learning.

NIST AI RMF

Govern, Map, Measure, Manage. The framework U.S. regulators and enterprise buyers now expect.

ISO 42001 & EU AI Act

AI management-system controls and obligations for high-risk systems, including transparency and oversight.

What you receive
Executive narrative

What an attacker can achieve against your AI, in language your board and your customers understand.

Reproducible findings

Full transcripts and exact conditions for every issue, so your engineers can confirm the fix themselves.

Architectural remediation

Tool scoping, identity propagation, retrieval-time authorization, and human-in-the-loop placement.

Attestation letter

Evidence for the security questionnaires that now ask how you test the AI in your product.

Mapped to OWASP Top 10 for LLM Applications, MITRE ATLAS, and NIST AI RMF.

Led by Offensive Engineers

Our AI work is led by operators from our red team and API testing practices. Prompt injection is the entry point. The impact always comes from the application, identity, and cloud layers underneath, and that is where deep experience matters.

Findings With Business Impact Attached

We don't deliver a list of clever jailbreaks. Every finding is chained to what an attacker gets: the records, the money, the access. Each one is rated on exploitability by an adversary working under realistic constraints.

Fixes Your Engineers Can Actually Ship

Architectural remediation: least-privilege tool design, identity propagation, retrieval-time authorization, output encoding, and human-in-the-loop placement. We work directly with your builders, including a joint remediation session.

Built for AI Release Velocity

AI features ship weekly, so annual testing is useless. We offer continuous AI assurance: regression suites that run against every model, prompt, and tool change, with a senior review each quarter.

AI Security Testing FAQ

Yes, and that is usually the higher-risk case, because nobody owns it. The exposure lives in the connections: which data it can reach, which employees can query it, what a vendor's AI feature does with your tenant data, and whether an attacker can influence it through content you don't control. Our AI attack surface discovery engagement is built exactly for organizations in this position.

Getting a model to say something rude is a party trick. Getting it to read an attacker-controlled document, retrieve another customer's data, and send it to an external address is a breach. Prompt injection matters in direct proportion to what the AI is connected to, so our testing focuses on impact chains. There is currently no complete fix for injection, so the defensible architecture is least privilege, identity propagation, and human approval on consequential actions.

A standard web application test covers the app around the model: auth, session handling, injection, access control. You still need it. It does not cover non-deterministic behavior, retrieval authorization, tool-call chains, or adversarial model manipulation. Most clients run both, and we scope them together so nothing falls between the two.

A focused LLM application test runs 1–2 weeks. Agentic systems with multiple tools and integrations typically run 2–4 weeks. AI attack surface discovery across an enterprise is a 2–3 week engagement. We quote fixed prices after a free 20-minute scoping call, and continuous AI assurance is available as a monthly retainer.

We prefer a production-equivalent staging environment with representative data and the full tool chain connected, because the tools are what create the risk. Where only production exists, we test under strict rules of engagement: no destructive tool calls, synthetic records where possible, approval gates for actions with real-world side effects, and full logging of every prompt we send so you can reconcile activity afterward.

Directly. You receive an attestation letter suitable for sharing with customers, findings mapped to OWASP LLM Top 10 and MITRE ATLAS, and evidence that supports NIST AI RMF and ISO/IEC 42001 narratives. For B2B SaaS companies, AI security testing has become one of the fastest ways to unblock enterprise deals.

Find Out What Your AI Can Be Talked Into

A 20-minute scoping call is enough for us to tell you where your AI exposure is concentrated and what testing would actually be worth doing.

Schedule an AI Security Scoping Call