Sunnyvale companies ship intelligence, and models fail in ways a classic application test never looks for. CyberFortify runs manual AI and LLM, API, cloud and web penetration tests here, aligned to the OWASP Top 10 for LLM Applications, the NIST AI RMF and SOC 2. Delivered remotely from our Gulf base on a daily overlap window, with on-site work where it genuinely helps. Fixed price, audit-ready reporting, free retest.
// 01 Why Sunnyvale businesses need penetration testing
Sunnyvale sits at the centre of the industry building modern AI: model companies, generative-AI startups, semiconductor and accelerator firms, and the data and MLOps platforms that feed them. The products leaving this city are not ordinary web apps - they are LLM-powered assistants, retrieval systems over private corpora, and agents wired to real tools and internal APIs. That intelligence is exactly the new attack surface.
A model does not enforce a boundary the way code does. A prompt-injection payload buried in a document, a web page or an email your agent later reads can override its instructions, exfiltrate the system prompt, or coax it into calling a tool it should never touch. Insecure output handling turns a helpful answer into stored XSS or an injected command when downstream code trusts model text. Sensitive data leaks straight back through a completion, and a RAG stack can hand one customer another customer's documents because access control was enforced before retrieval but not after.
Scanners and conventional app tests miss this entirely. They flag an outdated dependency; they cannot tell you that a crafted instruction makes your agent delete a record, that your vector store leaks across tenants, or that your model registry and training API keys sit exposed in a pipeline nobody hardened. Confirming those flaws takes a tester who understands both the model and the infrastructure it runs on.
// 02 Standards and regulatory drivers in Sunnyvale
AI security is standardising fast, and Sunnyvale companies are asked to evidence it by enterprise buyers, auditors and regulators alike. These are the anchors we most often map findings against.
OWASP Top 10 for LLM Applications
The reference list for LLM risk - prompt injection, insecure output handling, sensitive-data disclosure, excessive agency and more. We report AI findings against it so your reviewers recognise the language.
NIST AI Risk Management Framework
The NIST AI RMF frames how you govern, map, measure and manage AI risk. Independent testing feeds the Measure function with real evidence rather than self-attestation.
SOC 2 & ISO 27001
Selling AI into the enterprise means passing security review. SOC 2 reports and ISO 27001 A.8.29 evidence both rest on independent penetration testing before the contract closes.
CCPA / CPRA + CPPA
When personal data trains or reaches a model, California's privacy regime applies - including the CPPA's risk-assessment duties around automated decision-making. Our privacy-regulation guidance sets out the parallels.
ISO/IEC 42001 (emerging)
The first management-system standard for AI is becoming a procurement ask. Test evidence supports its control expectations as buyers start to require it alongside SOC 2.
NIST CSF & OWASP API Security Top 10
Your model ships inside an app and a set of APIs. We anchor that surrounding surface to NIST CSF and the OWASP API Security Top 10 - BOLA, broken auth and over-permissioned tokens.
// 03 Penetration testing services for Sunnyvale
Sunnyvale engagements lead with the model and the pipeline, because that is where the novel risk lives. AI and LLM testing leads for product and platform teams; API and cloud follow, since the agents, MLOps and inference stacks live there; web and network cover the rest of the surface.
AI & LLM pen testing
Prompt injection, jailbreaks, insecure output handling, model-extraction, RAG leakage and excessive-agency testing against the OWASP LLM Top 10 and NIST AI RMF.
API pen testing
Inference, agent-tool and MLOps APIs - broken object-level authorization, scope enforcement, token handling and rate abuse on the interfaces around your model.
Cloud pen testing
Identity, tenant isolation, storage exposure and service-account scope across the training, registry and inference infrastructure hosting your models.
Web application pen testing
The consoles, dashboards and chat front-ends around your model, tested against the OWASP Top 10 and business-logic abuse.
Network pen testing
External, internal and Active Directory testing, plus segmentation checks between corporate, training and production inference environments.
Red teaming
Goal-based adversary simulation - including AI-agent abuse and MLOps supply-chain scenarios - testing whether an intrusion is detected before models or data walk out.
// 04 How we deliver to Sunnyvale
We will not pretend otherwise: CyberFortify is a Gulf-based firm on UTC+3, and Sunnyvale sits ten to eleven hours behind us. We have no Silicon Valley office and no local staff. What we do have is a working pattern built around that gap - our late afternoon and evening is your morning, and we hold that window open daily for stand-ups, live triage and read-outs. Testing runs on while Sunnyvale is offline, so confirmed findings are waiting when your day starts.
What runs remotely
AI, LLM, API, cloud, web and external testing from our secure environment - the large majority of model, pipeline and application scope. Findings land in a shared channel as confirmed, and critical issues are escalated immediately.
What we do on-site
Internal network, wireless and segmentation testing where a tester genuinely needs to be on the wire, plus in-person workshops for AI and platform teams. We travel when it adds value and say so when it does not.
Every engagement opens with a free 30-minute scoping call and a fixed-price quote within the hour. For production inference and pipelines we agree test windows around release load, and a free retest proves the fixes.
// 05 Industries we secure in Sunnyvale
Sunnyvale's risk profile is shaped by a dense concentration of AI product companies, semiconductor firms and the data platforms that connect them.
// 06 Our methodology
Sunnyvale engagements follow the same audit-defensible process we run everywhere, tuned to the model at the centre of your product. Testing is grounded in the PTES and NIST SP 800-115, with AI work driven by the OWASP LLM Top 10 and NIST AI RMF, conventional exploitation mapped to MITRE ATT&CK, and the surrounding app driven by OWASP, including the API Security Top 10. As a CREST Accreditation Pathway firm we lead with manual testing - automation supports the tester, never replaces one.
Scoping & rules of engagement
Models, agents, tool integrations, data sources, test tenants and escalation paths agreed in writing first.
Fixed quote in 1hRecon & AI threat modelling
We map the model's trust boundaries - what it reads, which tools it can call, whose data it retrieves, and where output is trusted downstream.
OWASP LLM alignedManual exploitation
Prompt injection, jailbreaks, RAG leakage, agent abuse and MLOps exposure are exploited and chained under controlled conditions, using seeded data - never live customer data.
Controlled exploitReporting & free retest
Executive summary, CVSS-scored detail and mapping to the OWASP LLM Top 10, NIST AI RMF, SOC 2 or CCPA/CPRA - plus a free retest once fixes ship.
Audit-ready// 07 Why CyberFortify for Sunnyvale
A scan-and-report vendor
Automated output rebadged as a penetration test, blind to the model - unable to craft an injection, reason about agent permissions, or tell whether your RAG stack leaks across tenants.
CyberFortify
A Gulf-based, CREST-pathway team candid about the time difference and structured around it. Manual exploitation aimed at the model, the pipeline and the app together, findings mapped to the OWASP LLM Top 10 and NIST AI RMF, fixed pricing and a free retest.
Sunnyvale engagements most often pair an AI and LLM assessment with an API penetration test, since a model's risk splits between what it can be talked into and the interfaces and MLOps stack underneath it. Where a compromise would leak proprietary models or training data, we add red teaming to test detection under a realistic exfiltration scenario.
// 08 Frequently asked questions
Do you test LLM applications and AI agents, not just the web app around them?
Yes - the model and its agent are the primary target here, not an afterthought. We test for direct and indirect prompt injection, jailbreaks that bypass your guardrails, and insecure output handling where model text is rendered, executed or fed to a downstream tool without validation. For agents we probe excessive agency: whether a crafted instruction can make the agent call a tool, hit an internal API or take an action it should never be allowed to perform. We test the surrounding auth, authorization and API surface in the same engagement.
How do you test a RAG pipeline and its vector store?
We treat retrieval as an attack surface in its own right. We test whether one tenant's documents can surface in another tenant's answers, whether embeddings or the vector store leak content that should be filtered, and whether poisoned or attacker-planted documents can steer the model's responses once retrieved. We check that document-level access control survives the trip through embedding and retrieval, and that system prompts and source citations do not disclose data the user was never entitled to see.
Which standards drive AI penetration testing for Sunnyvale companies?
We anchor AI work to the OWASP Top 10 for LLM Applications and the NIST AI Risk Management Framework, which together name the failure classes assessors and enterprise buyers now ask about. SOC 2 covers the surrounding controls, and CCPA/CPRA - with the CPPA's risk-assessment duties - applies whenever personal data trains or reaches a model. ISO/IEC 42001 is emerging as the AI-management standard, and the conventional OWASP Top 10 and API Security Top 10 still govern the application your model ships inside.
You are not based in California - how does the time difference actually work?
We will be straight with you: CyberFortify is a Gulf-based firm on UTC+3, roughly ten to eleven hours ahead of Sunnyvale, with no Silicon Valley office and no local staff. We hold a deliberate daily overlap window - our late afternoon and evening lands in your morning - for stand-ups, live triage and read-outs. Testing runs on through your night, so confirmed findings are usually waiting when your team logs on.
How fast can we get a quote for a Sunnyvale engagement?
Book a free 30-minute scoping call and we return a fixed-price quote, usually within the hour and always within one business day. The report is written to hand straight to an auditor or an enterprise security reviewer, and a remediation retest is included once your fixes ship.