LLM and Agent Security Testing

AI Security and Red Teaming Services
Find AI Abuse Paths Before Production Exposure

Devlyn helps engineering, security, product, and compliance teams threat-model and red-team LLM applications, RAG systems, AI agents, copilots, extraction workflows, and model-powered automation. We test prompt injection, indirect prompt injection, unsafe tool use, data leakage, over-permissioned agents, retrieval abuse, insecure output handling, memory risks, cost abuse, and governance gaps, then turn findings into prioritized controls your team can implement.

Assess our AI attack surface See red-team scope

AI threat model

Assets, paths, controls

Scoped red team

Authorized testing only

Control roadmap

Fixes, evidence, owners

AI systems create attack paths traditional AppSec does not fully cover

A conventional penetration test can miss AI-specific behavior. The dangerous path may be hidden in a retrieved document, a tool description, a chat memory, a generated SQL query, an over-broad agent permission, or a response that a downstream system trusts too much.

What breaks

Untrusted content is mixed with instructions, so a model may treat hostile text from documents, pages, tickets, emails, or tool outputs as operational guidance.

AI agents are given broad tool access, but the product lacks deterministic authorization checks, approval steps, rate limits, audit logs, or safe rollback behavior.

RAG systems expose cross-tenant content, sensitive source snippets, hidden instructions, stale documents, confidential metadata, or evidence the user should not see.

Guardrails are treated as the security boundary even though the application still trusts model output for downstream actions, formatting, code, API calls, or business decisions.

Security teams cannot show customers or auditors an AI threat model, test evidence, residual-risk register, remediation plan, or owner map.

How Devlyn reduces risk

We map the AI application attack surface across prompts, retrieval, model calls, tools, agents, memory, files, logs, user roles, APIs, integrations, and downstream actions.

We run scoped adversarial testing aligned to your actual use case and common AI risk taxonomies such as OWASP LLM Top 10, NIST AI RMF GenAI Profile, and MITRE ATLAS where relevant.

We prioritize findings by business impact, exploit path, affected data, authorization weakness, reproducibility, observability, and remediation difficulty.

We recommend layered controls: input handling, retrieval scoping, tool authorization, output validation, human approval, logging, model routing, rate limits, data redaction, and incident playbooks.

We leave evidence your teams can act on: threat model, findings report, remediation backlog, control map, retest plan, and security handover notes.

What we deliver in AI security and red teaming

The engagement is scoped and authorized. We focus on finding realistic abuse paths and turning them into controls, not producing a long list of theoretical model weaknesses.

01

AI threat model

Map assets, data flows, user roles, model providers, prompts, retrieval sources, tools, memory, permissions, trust boundaries, abuse cases, and control gaps.

02

LLM and prompt-injection testing

Test direct and indirect prompt-injection risks, instruction hierarchy weaknesses, system-prompt exposure, unsafe output handling, and overreliance on model behavior.

03

RAG and data leakage review

Assess retrieval scoping, cross-tenant access, document permissions, poisoned content, sensitive metadata, citation leakage, source exposure, and vector-store boundaries.

04

Agent and tool-abuse testing

Review tool permissions, approval points, unsafe arguments, excessive agency, action authorization, loop behavior, audit logs, downstream writes, and rollback paths.

05

Guardrail and control assessment

Evaluate content filters, model policies, output validators, gateway controls, rate limits, redaction, allowlists, human review, and deterministic enforcement points.

06

Remediation and retest plan

Deliver prioritized findings, severity rationale, owner mapping, remediation backlog, retest criteria, residual-risk notes, and evidence-ready documentation.

AI attack surfaces we inspect

The risky surface is often the connection between the model and the rest of the application. We test the layers where language, data, permissions, and automation meet.

Prompt and instruction boundary

Assess how system instructions, developer messages, user inputs, retrieved content, and tool outputs are separated, logged, validated, and constrained.

Data and retrieval boundaries

Review source permissions, tenant isolation, document access, metadata exposure, source visibility, data retention, embedding inputs, and sensitive-output paths.

Tool and action authorization

Check whether AI-triggered actions are gated by user identity, role, intent, approval state, action type, transaction risk, and deterministic policy checks.

Memory and session state

Inspect session history, persistent memory, conversation carryover, user preferences, injected context, stale state, and cross-session contamination risks.

Output handling and downstream trust

Review generated code, generated SQL, structured outputs, instructions sent to other systems, HTML rendering, file creation, and API payloads.

Monitoring and incident response

Assess whether suspicious prompts, tool abuse, leakage, denial-of-wallet behavior, high-risk actions, and policy exceptions are observable and actionable.

Controls we commonly recommend

There is no single AI security control that covers every risk. The safer pattern is layered defense around the application, data, tools, users, and operating process.

01

Deterministic authorization

Move critical permissions out of prompts and into application-layer checks for user identity, role, tool, resource, transaction, and approval state.

02

Retrieval scoping and source hygiene

Limit retrieved content by identity, tenant, document status, source trust, metadata, freshness, and workflow context before it reaches the model.

03

Output validation and safe execution

Validate structured outputs, generated actions, file operations, code, SQL, API payloads, and commands before downstream systems trust them.

04

Prompt, log, and data redaction

Mask or avoid sensitive content in prompts, traces, feedback, analytics, screenshots, support tools, and long-term logs.

05

Human approval for high-impact actions

Require review, confirmation, escalation, or dual control before AI can trigger actions that affect money, access, compliance, safety, or customer state.

06

Abuse monitoring and response playbooks

Track suspicious input patterns, unusual tool calls, data exposure signals, runaway cost behavior, failed validations, and repeat attack attempts.

How the AI security engagement runs

We keep testing scoped, documented, and usable by engineering. Findings should lead directly to fixes, not sit in a PDF no one can operationalize.

We confirm systems in scope, testing boundaries, data restrictions, allowed accounts, environments, timing, evidence handling, and escalation paths.
Define scope and rules of engagement
We map assets, users, trust boundaries, prompts, retrieval sources, tools, model providers, integrations, logs, and likely abuse paths.
Build the AI threat model
We test scoped scenarios against the AI application surface, including prompts, RAG, agents, tools, output handling, data access, and monitoring.
Run adversarial test campaigns
We rank findings by impact, affected data, exploit path, control gap, reproducibility, business risk, and effort to remediate.
Triage findings with owners
We help define controls, validations, logging, approval flows, retrieval changes, gateway rules, policy checks, or architecture fixes where needed.
Design and support remediation
We retest priority fixes, document residual risk, hand over evidence, and create a security backlog for ongoing AI risk management.
Retest and hand over evidence

AI security and red teaming engagement models

Scoped options for teams that need AI-specific testing, remediation, and evidence before wider deployment.

Assessment

AI Security Assessment

Best before customer launch, enterprise review, or security signoff

Scoped

after discovery

Threat model

Scoped red team

Findings report

Remediation backlog

Talk to Sales
Most Popular

Remediation

AI Security Control Program

Best when findings need engineering support and retesting

Scoped

after discovery

Control design

Guardrail review

Tool authorization

Retest evidence

Talk to Sales

Ongoing

Continuous AI Security Testing

Best for active AI products with frequent model, prompt, or tool changes

Scoped

after discovery

Regression tests

New-risk review

Security backlog

Evidence updates

Talk to Sales

Who this service is for

AI security testing is most valuable when your AI system touches sensitive data, customer-facing workflows, business decisions, agent actions, or enterprise procurement reviews.

01

Pre-launch AI products

You are about to expose an LLM, RAG, copilot, extraction, or agent feature to customers and need AI-specific security evidence.

02

Enterprise SaaS teams adding agents

Your AI can read customer data, call tools, update records, summarize private content, or recommend actions inside a multi-tenant product.

03

Regulated or high-impact workflows

Your AI touches finance, healthcare, HR, legal, insurance, public-sector, security, safety, or compliance-sensitive operations.

04

Security teams asked about AI risk

Customers, auditors, executives, or procurement teams are asking how you test prompt injection, data leakage, tool abuse, and AI-specific controls.

Safe, authorized, and evidence-based testing

AI red teaming should improve security without creating uncontrolled risk. We define scope, protect sensitive data, and avoid publishing exploit details in public-facing material.

01

Rules of engagement

Testing starts only after environment, accounts, systems, timing, data handling, escalation, and evidence rules are agreed.

02

No uncontrolled exploit sharing

Reports include the evidence needed for remediation and retesting, while sensitive payload details and customer data are handled according to the engagement rules.

03

Privacy-aware evidence

We redact or minimize sensitive prompts, outputs, documents, logs, screenshots, and trace data where possible.

04

Residual-risk clarity

We do not claim AI risk can be eliminated. We document what was tested, what was fixed, what remains, and what should be monitored.

Test your AI system before real users do it for you

Share your AI workflow, model stack, data sources, tools, and launch timeline. We will help you scope a red-team assessment that produces findings your engineering and security teams can act on.

Assess our AI attack surface View AI governance service

hello@devlyn.ai

Threat model

Scoped red team

Remediation plan

Retest evidence

Frequently Asked Questions

Direct answers for teams comparing AI security assessments, LLM red teaming, prompt-injection testing, agent security, and RAG security reviews.

They include AI threat modeling, prompt-injection testing, RAG and data leakage review, agent and tool-abuse testing, guardrail assessment, output-handling review, findings report, remediation backlog, retesting, and handover documentation.

A normal penetration test focuses on traditional application vulnerabilities. AI red teaming also tests model behavior, untrusted content, retrieval, prompts, agents, tool calls, memory, output handling, data leakage, and overreliance on generated results.

Yes. We test direct and indirect prompt-injection risks within the agreed scope and focus on practical impact, such as data exposure, unsafe tool behavior, policy bypass, and downstream trust failures.

Yes. We review retrieval scoping, cross-tenant access, sensitive source exposure, poisoned content, source permissions, citation leakage, metadata exposure, and grounding-related abuse paths.

Yes. We test tool permissions, approval gates, unsafe arguments, excessive agency, retries, memory risks, downstream actions, audit logging, and rollback readiness.

Where relevant, we can map findings to OWASP LLM Top 10, NIST AI RMF and GenAI Profile, MITRE ATLAS, internal security controls, SOC 2 support, and customer security-questionnaire needs.

We can assess and recommend guardrails, but we do not treat guardrails as the only security boundary. Critical controls should also exist in application logic, authorization, validation, logging, and review workflows.

Yes. We can support remediation planning or implementation for retrieval scoping, tool authorization, output validation, logging, redaction, approval flows, gateway rules, and monitoring.

Only under agreed rules of engagement. Many tests can run in staging or controlled environments first, especially when sensitive data, customer workflows, or high-impact actions are involved.

We define evidence handling, redaction, access controls, data minimization, retention, and reporting rules before testing begins.

Yes. A structured AI threat model, findings report, remediation record, and control map can help answer customer security questions about AI-specific risks.

The typical scope is your AI application and how it uses models, tools, data, prompts, and workflows. Provider-level testing depends on contracts, permissions, and scope.

Yes. Retesting can verify whether priority controls reduce the tested abuse paths and whether residual risk is documented for ongoing monitoring.

No. The responsible goal is risk reduction, clear boundaries, measurable controls, monitoring, and documented residual risk, especially for systems that combine untrusted content with automation.