LLM red teaming and AI penetration testing is now offered by a mix of traditional cybersecurity firms that have added AI-specific practices and newer specialist vendors built around AI security from the start, and the right choice depends on whether the engagement needs broad application security coverage or deep LLM-specific expertise. A qualified vendor should combine automated adversarial testing, using tools such as Garak or Microsoft's PyRIT to run thousands of known attack patterns quickly, with manual testing by specialists experienced in prompt injection, jailbreaking, and training data extraction, since automated tools alone consistently miss creative, multi-turn attacks that a skilled human tester finds. Findings should be mapped to a recognized framework such as the OWASP Top 10 for LLM Applications and come with concrete remediation guidance, not just a severity-ranked list of problems with no path to fixing them. Before hiring a vendor, it is reasonable to ask for a redacted sample report and a description of their testing methodology, since the quality gap between a thorough manual-plus-automated engagement and a checkbox automated scan is large and not always obvious from a proposal alone. Ongoing testing, repeated as the application changes, matters more than a single pre-launch engagement. Nanobase AI runs LLM red teaming that pairs automated adversarial tooling with manual testing by its own security engineers.
Not all "red teaming" means the same thing
Two vendors can both describe their service as LLM red teaming or AI penetration testing while delivering very different depth of coverage, since the term is used for anything from a fully automated scan running known attack patterns to a sustained manual engagement by specialists probing for novel weaknesses. Understanding which testing approach a vendor actually delivers, before comparing price, is the difference between a genuinely useful security assessment and a checkbox exercise that misses the attacks a real adversary would try.
Testing approach comparison
| Approach | What it covers | Typical use case |
|---|---|---|
| Fully automated scan | Runs thousands of known attack patterns via tools like Garak or Microsoft's PyRIT | Fast, repeatable baseline coverage; misses creative, multi-turn attacks |
| Automated plus manual hybrid | Automated scanning combined with manual testing by specialists experienced in prompt injection and jailbreaking | Pre-launch assessment where genuine adversarial creativity matters |
| Continuous or ongoing testing | Recurring automated and periodic manual testing as the application changes | Production systems that change frequently and need testing to keep pace |
Automated tools alone consistently miss creative, multi-turn attacks that a skilled human tester finds, since known attack pattern libraries cannot anticipate every novel combination an attacker might try against a specific application's actual behavior; a hybrid approach is the more defensible standard for anything handling sensitive data or high-stakes decisions.
Questions to put in an RFP
These five questions, put directly to a vendor before a contract is signed, reveal more about actual coverage than any marketing description of the service.
- Does the engagement include manual testing by specialists, or automated scanning only, and what percentage of the total testing hours does each represent?
- Which framework are findings mapped against, ideally the OWASP Top 10 for LLM applications, so results are comparable across engagements and vendors?
- Can the vendor provide a redacted sample report from a prior engagement, showing the actual depth and format of findings delivered?
- Does the proposal include concrete remediation guidance for each finding, or only a severity-ranked list with no path to fixing the issues?
- Is the engagement a one-time assessment or does it include a defined cadence for retesting after remediation and as the application evolves?
Mapping findings to a standard, not just a report
Findings mapped to a recognized framework such as the OWASP Top 10 for LLM Applications are easier to track over time, easier to communicate to stakeholders outside the security team, and easier to compare across successive engagements or different vendors than an unstructured list of issues. A report that skips this mapping in favor of vendor-specific terminology is harder to act on and harder to demonstrate progress against during a subsequent audit or board review, which connects directly to how the organization would approach conducting its own red-teaming exercise internally as a complement to external testing.
Frequently asked questions
Is an automated-only scan ever sufficient on its own?
An automated scan provides useful baseline coverage and is far better than no testing at all, but for anything handling sensitive data, financial decisions, or public-facing interactions, it should be paired with manual testing rather than relied on as the complete assessment.
How much does the choice of vendor type affect pricing?
Pricing scales with the depth of manual testing involved; automated-only engagements are typically less expensive than a hybrid engagement, a distinction covered in more detail in how much an AI security audit costs.
Should red teaming happen once before launch or on an ongoing basis?
Ongoing testing, repeated as the application changes, matters more than a single pre-launch engagement, since new jailbreak and injection techniques circulate publicly on a rolling basis and a system that passed testing six months ago is not guaranteed to still pass today.
How Nanobase AI helps
Nanobase AI runs LLM red teaming that pairs automated adversarial tooling with manual testing by its own security engineers, mapping findings to the OWASP Top 10 for LLM Applications and providing concrete remediation guidance rather than a severity list alone. See the approach applied to a real system in a demo.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.