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Sprint projectJun 22, 2026Cali, Colombia

Beyond English: Assessing the Robustness of LLM Safety Mechanisms Against Structural Jailbreaks in Spanish

Jonathan Narvaez · Team Los Jailbreakers

Submitted to Global South AI Safety Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Beyond English: Assessing the Robustness of LLM Safety Mechanisms Against Structural Jailbreaks in Spanish

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LLM safety guardrails are trained mainly to catch direct, single-turn harmful requests in English, leaving open whether structural attacks — long-context framing and multi-turn escalation — work just as well in Spanish, particularly in dialects like Bogotá Spanish. This matters for Latin America's emerging AI governance frameworks, which assume safety guarantees never verified for Spanish deployments. We adapted 16 AdvBench-style harmful instructions into long-context and multi-turn attack formats and tested them against Gemini's standard chat interface under a black-box threat model. An exploratory session combining fictional framing with multi-turn escalation produced harmful content across all three tested categories — identity theft, violence-inciting content, and discriminatory speech — and across a broader set of 16 sub-objectives, 62.5% showed some degree of safety bypass. Gradual escalation, not framing alone, drove compliance, with partial-compliance responses acting as a foothold for further escalation rather than a safety backstop. While this single-session pilot lacks statistical power and a control group, it offers an existence proof that Spanish-language structural jailbreak vulnerabilities are real, motivating controlled replication as a priority for regional AI safety evaluation.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. Length vs. Content

    The paper is disproportionately long relative to the actual empirical contribution it delivers. A pilot study with a single session and no statistical results does not warrant the page count, and the redundant tables and repeated content suggest the length is largely artificial.

    No real metrics/position paper

    The paper presents itself as an empirical study but delivers no quantitative results in any defensible sense. The headline 62.5% bypass figure comes from one uncontrolled session with manual, non-blinded classification. It reads as a position paper and existence proof, which is a valid contribution, but should be framed honestly as such.

    Missing control conditions

    The methodology explicitly calls for comparing English, neutral Spanish, and Bogotá Spanish, but none of that comparison was actually conducted. The core research question of whether Spanish-language structural jailbreaks behave differently from English ones remains entirely unanswered.

    Table formatting

    The results tables have no numbers, no proper captions, appear duplicated across sections with minor variations, and are full of wasted whitespace. A single consolidated table would communicate the same information far more clearly.

    What works

    The limitations section is unusually honest and self-aware for a student paper, the related work is well-contextualized, and the research question is genuinely relevant to Latin American AI governance. The bones of a good future study are here.

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  2. The paper addresses an important and fascinating question regarding the success of structural jailbreaks in languages other than English, but the current experiments do not clearly establish whether jailbreak success actually differs across English, neutral Spanish, and more strongly marked dialectal varieties. The authors acknowledge this limitation themselves. However, without a direct comparison of attack success rates, it remains unclear whether the observed effects reflect changes in model vulnerability. While the paper is clearly presented and openly discusses its limitations, the experimental design does not directly test the central hypothesis. Given the limited duration of the hackathon, a smaller set of jailbreaks evaluated systematically across these language varieties might have provided stronger evidence for the paper's main claim and increased the practical relevance of the findings.

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  3. El proyecto aborda una pregunta relevante para la seguridad de modelos de lenguaje en contextos hispanohablantes: si los mecanismos de seguridad resisten ataques estructurales cuando se realizan en español, y no solo en inglés o frente a solicitudes dañinas directas. Su intuición principal me parece valiosa, no debería asumirse que un modelo es seguro para usuarios hispanohablantes si no ha sido probado en sus idiomas y formas reales de interacción, y su mayor mérito está en trasladar al español una familia de ataques ya conocida (contexto largo, ficción y escalamiento conversacional) y mostrar, en una interacción real, que esa vulnerabilidad puede aparecer. Sin embargo, el proyecto no propone una técnica nueva ni demuestra de forma controlada una vulnerabilidad específica del español bogotano frente al inglés o al español neutro: percibo que funciona mejor como una prueba exploratoria de existencia que como una evaluación comparativa sólida.

    La principal debilidad está en la ejecución, y el propio equipo reconoce con honestidad varias limitaciones: es una sesión única, sin grupo control, que combina varias técnicas sin aislar cuál produjo el bypass, con clasificación hecha por el mismo equipo y sin probar mitigaciones. Valoro esa transparencia, pero las limitaciones no son menores. A esto se suma que el documento plantea una comparación entre inglés, español neutro y bogotano, pero los resultados visibles provienen de una sola sesión contra un solo modelo, que el propio texto diferencia de la comparación cuantitativa prometida, y que las cifras reportadas se contradicen entre sí, lo que impide reconstruir con claridad cuál es el resultado consolidado. La extensión del anexo no debe confundirse con una muestra amplia: documenta una interacción larga, no múltiples pruebas independientes.

    Desde mi eje de auditoría y rendición de cuentas, percibo un punto especialmente importante, porque toca la coherencia entre lo que el trabajo predica y lo que practica. El documento afirma, al describir su sesión exploratoria, que no reproduce las salidas; sin embargo, el anexo incluye salidas transcritas como "verbatim", extensas y asociadas a categorías sensibles. Aun si correspondieran a tandas distintas, el documento no establece esa distinción ni justifica por qué unas se manejan con contención y otras se publican íntegras. Para un trabajo centrado en seguridad, sentí que esta tensión debería corregirse con una política clara de divulgación responsable. En esa misma línea, el anclaje regulatorio se queda en la mención: invoca marcos regionales para justificar su relevancia, pero no identifica qué principio o responsabilidad institucional concreta queda tensionada por el hallazgo.

    Para fortalecer el proyecto, el siguiente paso sería convertir esta alerta en un protocolo más controlado y reutilizable: comparar los mismos ataques en inglés, español neutro y variantes locales; ampliar modelos y categorías de daño; separar el efecto de cada técnica; incorporar jueces independientes; probar mitigaciones mínimas; y establecer una política de divulgación responsable que el propio reporte cumpla.

    Y quiero dejar una reflexión, con respeto por el trabajo real que hay detrás y por la honestidad con que el equipo reconoce sus límites: la tesis del proyecto (que la seguridad no puede afirmarse sin verificarla), me parece valiosa, y justamente por eso percibo que es una invitación que el reporte podría hacerse también a sí mismo, aplicando a su propia evidencia el mismo rigor que reclama para los modelos. En conjunto, el proyecto tiene valor como primer paso y como advertencia frente a la transferencia automática de garantías de seguridad del inglés al español; su aporte debe mantenerse proporcional a la evidencia, que todavía es exploratoria.

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Cite this project

@misc{narvaez2026beyond,
  title = {{Beyond English: Assessing the Robustness of LLM Safety Mechanisms Against Structural Jailbreaks in Spanish}},
  author = {Jonathan Narvaez},
  year = {2026},
  month = jun,
  note = {Submitted to Global South AI Safety Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/beyond-english-assessing-the-robustness-of-llm-safety-mechanisms-against-structural-jailbreaks-in-spanish-s4zk}},
  url = {https://apartresearch.com/sprints/projects/beyond-english-assessing-the-robustness-of-llm-safety-mechanisms-against-structural-jailbreaks-in-spanish-s4zk}
}

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