AI penetration testing is a security assessment of your LLM applications and agents. Our testers attempt prompt injection, data leakage, jailbreaks and unsafe tool use, map findings to the OWASP Top 10 for LLM applications and MITRE ATLAS, then verify your fixes.
Book discovery callA user or a poisoned document tells the model to ignore its rules. It complies, and your system prompt or policy no longer holds.
Retrieval pulls records the user has no right to see, or the model repeats secrets from its context. Nobody notices until a customer reports it.
An agent with access to email, tickets or a database can be steered into sending, deleting or changing things outside its task.
Standard web and network tests check endpoints and configs. They don’t test how a model reasons over hostile input.
We craft inputs that try to override your system prompt, both typed by users and hidden in documents, web pages, emails or tool outputs the model reads. Multi-turn escalation and system prompt extraction are part of every engagement.
We test whether the model reveals personal data, credentials, internal documents or other tenants’ records. That includes retrieval that ignores user permissions, verbose error messages and secrets left in prompts, logs or vector stores.
We try to steer agents into calling tools outside their task, chaining tools into unauthorized outcomes or acting on poisoned tool descriptions. We check whether permissions follow least privilege and whether risky actions need human confirmation.
We run role-play, encoding, language-switching and multi-step jailbreak techniques against your content filters and policy layer. Each successful bypass is logged with the exact prompt chain so your team can reproduce and patch it.
We check what happens to model output downstream: rendered markdown that executes script, generated SQL or code passed to an interpreter, and links that leak data. These turn a model mistake into a classic application vulnerability.
We test whether an attacker can plant content in the sources your retrieval system indexes and use it to change answers or trigger actions. We also review ingestion controls, document permissions and how sources are cited.
We probe rate limits, cost controls, authentication and tenant isolation on your model endpoints. That covers denial-of-wallet attacks, key exposure in client code and gaps between your API gateway and the model provider.
Tell us what it does and we’ll propose a test scope within two business days.
Request a test scopeWe map your AI system: models, prompts, retrieval sources, tools, permissions and data flows. We agree on attacker profiles, test boundaries and rules of engagement with your security lead.
Deliverables: Threat model, attack surface map, rules of engagement
Our testers run manual and automated attacks across the OWASP Top 10 for LLM applications and relevant MITRE ATLAS techniques. Critical findings are reported to you the same day.
Deliverables: Test logs, reproduction prompts, critical finding alerts
We rank every finding by exploitability and business impact, with proof of concept and a specific fix. We walk your engineers through the report and answer questions.
Deliverables: Findings report, risk ranking, remediation guidance
Once your team has applied fixes, we rerun the original attacks and variants of them. We close findings only when the attack no longer works.
Deliverables: Retest report, closure evidence, regression prompt set
Testers think like an adversary and chain techniques, which surfaces issues a checklist or scanner run misses.
Findings are ranked by exploitability and business impact, so your engineers know where to start.
A retest reruns each attack against your patched system and closes findings only on evidence.
Findings map to OWASP and MITRE ATLAS, which gives security reviewers and auditors a recognized reference.
We hand over the regression prompt set, so your team can rerun the attacks after each model or prompt change.
Ready to see this in your own workflows?
Book discovery callHere is how automated scanning, a traditional pentest and a tkxel AI penetration test differ for LLM applications and agents.
| Criterion | Automated AI scanner | Traditional pentest | tkxel AI penetration test |
|---|---|---|---|
| Prompt injection coverage | Known payload libraries | Rarely in scope | Manual and automated, multi-turn |
| Agent tool misuse | Limited | Not covered | Tested against real tool permissions |
| Business logic context | None | App and network only | Threat model of your workflows |
| Framework mapping | Varies by tool | OWASP web, network | OWASP LLM, MITRE ATLAS |
| Fix verification | Rerun scan | Optional retest | Retest included |
| Fails when | Novel attacks aren’t in library | Model behavior is out of scope | Model changes after the test |
“Placeholder quote — replace with a real client quote.”
“Placeholder quote — replace with a real client quote.”
“Placeholder quote — replace with a real client quote.”
We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.
Each pod is led by an engineer who has shipped AI systems before, drawn from tkxel’s team of around 150 AI professionals. The same people stay through delivery.
Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.
Every engagement starts with agreed business metrics and evaluation sets. We report against them throughout, and the pod exits when your team can run the system.
“Placeholder testimonial — replace with the global set from tkxel.com.”
“Placeholder testimonial — replace with the global set from tkxel.com.”
AI penetration testing is a security assessment where testers attack your LLM applications, retrieval systems and agents to find exploitable weaknesses before real attackers do. It focuses on risks a traditional pentest does not cover, such as prompt injection, sensitive data leakage, jailbreaks and agents misusing their tools. Findings are ranked by risk, mapped to the OWASP Top 10 for LLM applications and MITRE ATLAS, and verified again after your team fixes them.
AI penetration testing checks a defined system for exploitable vulnerabilities within an agreed scope, while AI red teaming simulates a goal-driven adversary across a wider surface. In practice the terms overlap, and many buyers use them interchangeably. Our engagements combine both: a scoped test of your LLM application or agent, plus adversarial scenarios such as getting the agent to send data outside your company, chained across prompts, documents and tools.
Prompt injection is an attack where crafted text makes a model ignore its original instructions and follow the attacker’s instead. Direct injection comes from what a user types; indirect injection hides instructions in content the model reads, such as documents, emails or web pages. We test both, including multi-turn escalation and system prompt extraction, and record the exact prompt chain for every successful attack so your team can reproduce it.
Yes, we use the OWASP Top 10 for LLM applications as the baseline checklist and map each finding to it. We also map techniques to MITRE ATLAS, which catalogs adversary tactics against AI systems, and to OWASP’s agentic AI guidance when agents are in scope. The frameworks set minimum coverage, but our testers also build attacks around your specific workflows and permissions, since that is where the most damaging issues usually sit.
A typical AI penetration test takes 2–4 weeks from threat modeling to final report, plus a retest once your fixes are in. A single chatbot with one retrieval source sits at the short end. A multi-agent system with several tools, data sources and user roles needs longer. Critical findings are reported the same day we confirm them, so you do not wait for the final report to start fixing.
No, a penetration test cannot make an AI system fully secure; it shows what was exploitable at the time of testing. LLM behavior is probabilistic, and a model update, prompt change or new tool can reopen issues we closed. That is why we hand over a regression prompt set and recommend retesting after major changes. Treat the test as one control alongside input filtering, least-privilege tool access and monitoring.
Testing does not have to touch production; we prefer a staging environment that mirrors your production models, prompts, tools and permissions. If only production is available, we agree on rules of engagement first, such as test accounts, rate limits, blocked destructive actions and a named contact who can stop testing. Agents that can send email, move money or delete records are tested with those actions sandboxed or stubbed.
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