The critical gap in most AI safety programs is not English — it is every other language.
Into23 provides native-speaker multilingual adversarial testing across APAC languages. Code-switching attacks succeed 46.7% more often than English-only attacks. Low-resource languages are approximately three times more likely to generate harmful content under attack conditions. We test where your current safety program stops.
Pricing is scoped individually based on language coverage, prompt volume, attack taxonomy, and reporting requirements. Contact us for a custom quote.
Our testers craft adversarial prompts that exploit linguistic nuance, regional slang, dialect variation, code-switching, and culturally specific framings — attack vectors that automated translation and English-only testing cannot replicate.
Code-switching attacks — where prompts blend two or more languages — succeed 46.7% more often than English-only attacks. We test these systematically across your target language pairs to surface the gaps your current safety program is missing.
Low-resource languages are approximately three times more likely to generate harmful content under attack conditions due to thinner safety training data. We prioritise these languages in scoped engagements to address the highest-risk gaps first.
Native-speaking testers identify harmful phrasing, local sensitivities, religious and political content risks, and market-specific failure modes that generic testing teams and translated test sets consistently miss.
Every finding is documented with the original prompt, the model output, a severity rating, and practical notes on reproducibility — structured so product, safety, and compliance teams can act quickly.
Structured testing reports and documented methodology can support compliance dossiers under the EU AI Act, NIST AI RMF, ISO 42001, and China's Interim Measures on Generative AI — though Into23 does not provide legal compliance consulting.
Native-speaker testers with deep cultural fluency — not translated English test sets.
Additional languages available via vetted contractor networks. Coverage confirmed at scoping stage.
We align on model type, user journeys, target languages, safety policies, and the failure modes that matter most commercially and operationally — with particular focus on low-resource languages and code-switching risk.
Our team executes adversarial prompt sets across agreed languages, markets, and scenario families using testers with deep cultural and linguistic fluency — not translated English test sets.
Each issue is documented with prompts, outputs, severity classification, and practical notes on reproducibility. Findings are grouped by risk level and likely business impact.
We present results in a format product, safety, and leadership teams can use to prioritise what to fix before launch — with clear separation between critical, high, and lower-priority findings.
The EU AI Act, NIST AI RMF, ISO 42001, and China's Interim Measures on Generative AI all mandate adversarial testing and safety assessments. Most enterprises currently operate at Level 1 or 2 of multilingual red teaming maturity — English-only or ad hoc. Into23's structured multilingual testing methodology supports the documentation required to demonstrate compliance-adjacent safety coverage across jurisdictions.
Note: Into23 provides testing documentation and methodology. We do not provide legal compliance consulting or regulatory filing support.
Into23 specialises in the multilingual and cultural dimension of adversarial testing. The following are outside our scope — we can recommend appropriate specialist partners for these needs.
Standard red teaming typically runs in English and misses language-specific safety gaps, culturally sensitive failure modes, and risks that only appear in other languages. Research shows code-switching attacks succeed 46.7% more often than English-only attacks, and low-resource languages are approximately three times more likely to generate harmful content under attack conditions. Native-speaker testers find issues that translated test sets cannot.
Our primary coverage includes Bahasa Indonesia, Bahasa Malaysia, Cantonese, Hindi, Japanese, Korean, Mandarin, Tagalog, Thai, and Vietnamese. Additional languages are available via vetted contractor networks. Coverage is confirmed at scoping stage based on your specific requirements.
Enterprise AI teams preparing for product launches in APAC markets, regulated deployments requiring documented safety testing, and AI companies that need to demonstrate multilingual safety coverage to enterprise buyers or regulators. We also work as a subcontractor partner for larger AI safety programs that lack in-house multilingual capability.
Into23 specialises in the multilingual and cultural dimension of adversarial testing. We do not provide technical red teaming (prompt injection, agent attacks), automated red tooling, multimodal red teaming, or legal compliance consulting. For those needs we can recommend appropriate specialist partners.
We offer pilot projects (typically 2–4 weeks, 2–3 languages), project-based engagements for pre-launch testing, retained partnerships for ongoing safety programs, and subcontractor arrangements for larger AI safety providers. Pricing is scoped individually based on language coverage, prompt volume, attack taxonomy, and reporting requirements.
Get a custom quote for your multilingual AI safety testing program. Our team typically responds within 24 hours.