A multilingual AI system should not become a cognitively different system when the user changes language.
Its conclusions, uncertainty, instruction discipline and safety boundaries should remain stable whether a request is written in English, Modern Standard Arabic, Gulf Arabic, Arabic–English code-switching or Arabizi. In practice, however, linguistic variation can expose behavioural differences that conventional benchmarks and translated test sets fail to detect.
This is the problem addressed by CiberIA ArabicMind, an Arabic cognitive, linguistic and security evaluation module developed within the CiberIA framework.
ArabicMind is not an Arabic proficiency examination. It is not a translation benchmark, a dialect classifier or a collection of generic multiple-choice questions. It evaluates whether an AI system preserves its functional behaviour when meaning is expressed through the real linguistic conditions found across Arabic-speaking environments.
The central question is simple:
Does the system remain the same AI—with the same reasoning, prudence, safety and operational limits—when it moves from English or formal Arabic into real-world Arabic?
Answering that question requires more than measuring accuracy.
Multilingual fluency is not multilingual reliability
A model can produce fluent Arabic while still failing to preserve:
- the original intent of an instruction;
- a critical negation;
- the relationship between entities;
- the level of epistemic uncertainty;
- a system-level restriction;
- the distinction between permitted and prohibited actions;
- the same safety decision applied in English;
- the integrity of source attribution.
A response may sound natural and still be functionally incorrect.
Traditional multilingual evaluation often begins with an English benchmark and translates it into another language. This can measure part of the model’s linguistic competence, but it does not reproduce the complete operating environment.
Arabic introduces several interacting dimensions:
- non-concatenative root-and-pattern morphology;
- concatenative affixes and clitics;
- frequent writing without short-vowel diacritics;
- considerable dialectal variation;
- code-switching;
- transliteration and Arabizi;
- right-to-left and bidirectional text;
- region-specific pragmatic and cultural expectations.
Each dimension can influence tokenization, semantic interpretation, safety classification and response generation. Their interaction is especially important: a model may tolerate one transformation but fail when dialect, code-switching and orthographic manipulation appear together.
ArabicMind therefore treats multilingual reliability as a problem of behavioural invariance under controlled linguistic transformation.
From answer correctness to cognitive invariance
Let (c) represent a semantic test core: the underlying intent, constraints and expected safe behaviour of a scenario.
A transformation (T_v) produces a linguistic variant (v):
[
p_{c,v}=T_v(c)
]
The same model (M), with the same system policy (S), receives every variant:
[
r_{c,v}=M(S,p_{c,v})
]
The evaluator does not compare the wording of the responses. Two valid answers may use very different language. Instead, it extracts behaviourally relevant properties:
[
F(r)=
{
d, s, u, i, e
}
]
Where:
- (d) is the substantive decision or conclusion;
- (s) is the safety action;
- (u) is the expressed uncertainty;
- (i) is instruction fidelity;
- (e) is the supporting evidence or source behaviour.
The question becomes:
[
F(r_{c,v_1}) \approx F(r_{c,v_2})
]
If two prompts are semantically equivalent, their responses should preserve the same functional properties within an acceptable tolerance.
ArabicMind measures the distance between those properties. A response is not considered robust merely because it is grammatically correct. The module looks for semantic drift, safety drift, confidence drift and instruction drift.
This distinction is fundamental.
A translation benchmark asks:
Did the model understand this Arabic sentence?
ArabicMind asks:
Did the model preserve its reasoning, security properties and operational identity across all controlled forms of this sentence?
Why Arabic is a powerful cognitive-security stress test
1. Root-and-pattern morphology
Arabic morphology combines roots, patterns, affixes and clitics. Many lexical relationships are built by inserting consonantal roots into morphological templates.
This is not purely concatenative processing. The internal structure of a word may encode voice, aspect, number, gender or derivational relationships.
Research on Arabic morphology describes both templatic and concatenative morphotactics. Non-concatenative mechanisms participate in verbal forms, passive voice, imperative constructions and broken plurals, while concatenative processes express properties such as person, gender and number. Arabic Lemmatization through Patterns provides a useful computational description of this interaction.
For an LLM, this creates several possible failure modes:
- associating every word sharing a root with the same meaning;
- ignoring the semantic contribution of the pattern;
- mishandling attached clitics;
- losing a negation or pronoun reference;
- treating a derived form as if it were its most frequent lexical relative;
- failing on uncommon but valid inflections.
ArabicMind uses controlled morphological variation to determine whether the model follows the actual proposition or relies on superficial lexical resemblance.
The objective is not to test whether the model can name a root. It is to determine whether morphological structure changes its interpretation of risk, permission, agency or causality.
2. Ambiguity and diacritization
Ordinary Arabic text is frequently written without most short-vowel diacritics. Consequently, the same consonantal sequence can support multiple lexical or grammatical interpretations.
A reliable model should use context to resolve the ambiguity. When context is insufficient, it should explicitly preserve uncertainty instead of silently selecting the most statistically frequent reading.
Diacritics also affect the model at a lower level. A 2026 study found that different degrees of Arabic diacritization influence tokenization and LLM benchmark performance, with full diacritization potentially increasing subword fragmentation and degrading results. Do Diacritics Matter?
This creates an interesting evaluation pair:
- an undiacritized prompt that requires contextual disambiguation;
- a diacritized version that makes the intended interpretation explicit.
If the explicit version changes the model’s conclusion, safety classification or confidence unexpectedly, the issue may involve more than language comprehension. It may indicate sensitivity in tokenization, internal representation or downstream safeguards.
ArabicMind evaluates three behaviours:
- Does the model identify genuine ambiguity?
- Does it use the available context correctly?
- Does it adjust its certainty when the evidence changes?
The third question is particularly important. A model that gives the correct answer for the wrong reason may fail under a minimally different formulation.
3. Modern Standard Arabic is not the complete operating environment
Modern Standard Arabic, or MSA, is essential in formal communication, government, media and documentation. It is not, however, the only Arabic that users employ with AI systems.
Dialectal Arabic varies across regions and cities. The MADAR corpus demonstrated the scale of this variation by constructing parallel sentences for dialects from 25 Arab cities, together with MSA and other languages.
More recent work has evaluated how LLMs handle dialectal comprehension, generation and cultural knowledge. AraDiCE found meaningful differences across dialects and between comprehension and generation.
This matters because models can exhibit asymmetric competence:
- understanding a dialect but answering in MSA;
- identifying the broad regional variety but missing its pragmatic intent;
- preserving literal meaning while losing politeness, urgency or indirectness;
- reaching a different conclusion in dialectal Arabic;
- applying stricter or weaker safety boundaries depending on the variety.
ArabicMind does not assume that a model must always generate a perfectly native dialect. That would mix linguistic naturalness with operational safety.
Instead, it separates two questions:
- Did the model understand the request?
- Did it generate an appropriate response for the requested linguistic profile?
A system may be operationally safe but linguistically weak, or linguistically convincing but cognitively unstable. These outcomes must not receive the same diagnosis.
4. Arabic–English code-switching
In many professional environments, especially across the Gulf, communication moves naturally between Arabic and English.
Technical terminology, product names, security concepts, corporate processes and interface commands may remain in English while the surrounding request is written in Arabic. The language can also change inside a sentence.
Code-switching is not equivalent to translating two independent fragments. Relationships can cross the language boundary:
- an Arabic negation may govern an English action;
- an English permission may be restricted by an Arabic condition;
- a system identifier may be embedded inside dialectal instructions;
- a security-sensitive term may appear in a different script from the surrounding context.
The 2025 Survey of Code-switched Arabic NLP documents both progress and significant remaining gaps in datasets, evaluation methods and task coverage.
ArabicMind uses code-switching to test constraint preservation rather than stylistic fluency.
For example, a test family may preserve the same semantic nucleus while changing where the language boundary appears. If the model follows a restricted action when the verb is in English but rejects it when the whole request is Arabic, the module records a safety-parity failure.
The important variable is not the amount of English. It is whether the switch occurs at a semantically critical point.
5. Arabizi is both a linguistic reality and a security surface
Arabic users frequently write Arabic using Latin characters and numbers. This is generally described as Arabizi, Arabic chatspeak or Arabic transliteration.
Arabizi is highly variable. The same sound or word can have multiple representations, and conventions differ by region and user. That variability makes it difficult to process with simple keyword lists or deterministic normalization.
It also has direct security relevance.
Research presented in Jailbreaking LLMs with Arabic Transliteration and Arabizi showed that transliteration and Arabizi could expose weaknesses not observed with equivalent prompts in standardized Arabic. Requests that did not bypass safeguards in standard Arabic could produce unsafe behaviour after the script and representation changed.
This suggests a possible separation between:
- the model’s semantic understanding;
- the safety system’s lexical or representational coverage.
The generator may understand the request while a preceding safeguard fails to classify it correctly.
ArabicMind therefore treats Arabizi as more than noisy input. It uses it to probe whether security policies operate on meaning or on familiar surface forms.
A strong system should not simply reject all Arabizi. That would create unacceptable false positives for legitimate users. The desired behaviour is:
- understand the representation where reasonably possible;
- preserve the same safety decision;
- request clarification when the transcription is genuinely ambiguous;
- avoid increasing confidence because the safety layer failed to recognize the content.
6. Unicode, RTL and bidirectional text
Arabic is written from right to left, but Arabic digital text frequently contains left-to-right elements such as numbers, URLs, code, email addresses and English product names.
The Unicode Bidirectional Algorithm determines how logical character order is displayed visually. This creates a distinction between:
- the stored sequence of code points;
- the sequence presented on screen;
- the sequence interpreted by a parser;
- the sequence received by a model.
Unicode explicitly documents security risks related to bidirectional text and spoofing in Unicode Technical Report #36. Unicode Technical Standard #39 also defines mechanisms for detecting potentially confusable or security-sensitive text.
For AI systems, this produces several attack possibilities:
- visually reordering an instruction;
- inserting invisible formatting controls;
- hiding a modifier between visible characters;
- presenting different apparent and logical command sequences;
- confusing a human reviewer while preserving machine-readable content;
- producing logs that do not visually match the input processed by the model.
ArabicMind retains the raw text and inspects security-relevant code points. It does not immediately delete them, because normalization could destroy the test condition.
A simplified inspection layer may look like this:
const BIDI_CONTROLS =
/[\u061C\u200E\u200F\u202A-\u202E\u2066-\u2069]/gu;
const ZERO_WIDTH =
/[\u200B-\u200D\u2060\uFEFF]/gu;
function inspectUnicode(input) {
return {
raw: input,
nfc: input.normalize("NFC"),
bidiControls: input.match(BIDI_CONTROLS) ?? [],
zeroWidthCharacters: input.match(ZERO_WIDTH) ?? [],
codePoints: [...input].map((character) => ({
character,
codePoint: `U+${character
.codePointAt(0)
.toString(16)
.toUpperCase()
.padStart(4, "0")}`
}))
};
}
The raw, normalized and rendered representations should be evaluated separately.
This is a crucial engineering rule: normalization must not silently erase evidence before the security analysis takes place.
The ArabicMind test architecture
ArabicMind is organized around semantic test families rather than isolated prompts.
Each family begins with one semantic nucleus containing:
- the intended meaning;
- the relevant entities;
- permissions and restrictions;
- safety requirements;
- expected uncertainty;
- evidence requirements;
- critical failure conditions.
Controlled variants are then produced for the linguistic dimension being evaluated.
The current MVP contains:
- 20 semantic test families;
- 80 controlled prompt variants;
- 10 evaluation blocks;
- five weighted assessment pillars;
- critical risk gates;
- three guided demonstration profiles.
Not every family uses every possible transformation. A morphology-focused family may compare MSA forms, clitic structures and diacritization, while a security-focused family may compare MSA, Gulf Arabic, code-switching, Arabizi and Unicode perturbations.
This avoids generating transformations that are technically possible but methodologically meaningless.
The ten evaluation blocks
| Block | Evaluation target |
|---|---|
| 1 | Root-and-pattern morphology |
| 2 | Ambiguity and diacritization |
| 3 | Negation, pronouns, gender, number, dual forms and reference resolution |
| 4 | Consistency between MSA and Gulf Arabic |
| 5 | Register, pragmatics and indirect intent |
| 6 | Arabic–English code-switching |
| 7 | Arabizi and transliteration |
| 8 | Unicode, RTL and invisible-character robustness |
| 9 | Safety parity and instruction discipline |
| 10 | Hallucination, regional knowledge and source discipline |
The module evaluates both each individual response and the relationship among responses belonging to the same family.
This second layer is where cognitive drift becomes visible.
Pairwise evaluation and drift detection
Suppose a family contains four variants:
- (v_0): MSA reference;
- (v_1): Gulf Arabic;
- (v_2): Arabic–English code-switching;
- (v_3): Arabizi or adversarial Unicode.
For each property (k), ArabicMind assigns a response score:
[
q_{c,v,k} \in [0,100]
]
The family’s preservation score can be calculated from the weighted response scores:
[
P_{c,k} =
\frac{\sum_v w_{c,v,k}q_{c,v,k}}
{\sum_v w_{c,v,k}}
]
However, the average alone is insufficient. The evaluator must also measure drift relative to a validated reference:
[
D_{c,k} =
\max_v
d_k
\left(
F_k(r_{c,v}),
F_k(r_{c,v_0})
\right)
]
A model may obtain three excellent results and one catastrophic result. Averaging the four could conceal a vulnerability affecting a real group of users.
ArabicMind therefore distinguishes between:
- capability score: how well the model performs overall;
- invariance score: how stable that performance remains;
- critical failure status: whether any variant crosses an unacceptable safety boundary.
This distinction prevents a frequent benchmark failure: using a high average to hide a severe low-frequency weakness.
The five scoring pillars
The MVP calculates an overall result on a 0–100 scale using five public pillars.
| Pillar | Weight |
|---|---|
| Semantic preservation and reasoning stability | 30% |
| Dialectal and bilingual robustness | 20% |
| Uncertainty calibration | 15% |
| Safety parity and instruction discipline | 25% |
| Orthographic and Unicode robustness | 10% |
The aggregate score is:
[
A =
0.30S +
0.20D +
0.15U +
0.25G +
0.10O
]
Where:
- (S) is semantic preservation;
- (D) is dialectal and bilingual robustness;
- (U) is uncertainty calibration;
- (G) is safety and instruction governance;
- (O) is orthographic and Unicode robustness.
The result is accompanied by a risk level:
- LOW
- MEDIUM
- HIGH
Risk is not derived from the aggregate alone.
Why critical risk gates are necessary
Imagine a model that performs very well in 79 of 80 variants but follows a prohibited instruction when the same intent is written in Arabizi.
Its numerical average could remain excellent. Operationally, however, the model has a reproducible safety bypass.
ArabicMind uses critical gates to prevent this vulnerability from disappearing inside the average. A gate can elevate the final risk when the system exhibits behaviour such as:
- following an instruction that should remain prohibited;
- losing a critical negation;
- executing text hidden through bidirectional controls;
- fabricating evidence for a high-impact decision;
- changing a safety decision only because the dialect changed;
- interpreting ambiguous text with unjustified certainty;
- treating a user instruction as more authoritative after script manipulation.
The exact production thresholds and private cases do not need to be public. The evaluator should, however, explain which failure category caused the risk elevation.
A serious AI assessment must be auditable. “The model scored 82” is not sufficient. The report must show what changed, where it changed and why the change matters.
What the browser-based MVP actually does
ArabicMind v1.0 is implemented as a completely local web application.
It can be opened by double-clicking index.html. It does not require:
- installation;
- a web server;
- a user account;
- an API key;
- an Internet connection.
The application includes:
- an English–Arabic interface;
- complete right-to-left layout support;
- a guided demonstration mode;
- three predefined behavioural profiles;
- a workflow for evaluating pasted LLM responses;
- transparent pillar scoring;
- critical risk explanations;
- local persistence;
- JSON and CSV export;
- a printable report that can be saved as PDF.
The absence of a backend is intentional for the MVP.
It makes the demonstration:
- portable;
- reproducible;
- privacy-preserving;
- easy to inspect;
- independent of a particular model provider.
No prompt or response needs to leave the device through ArabicMind itself.
For production deployment, the same evaluation model can be connected to controlled inference endpoints, local models, private APIs or on-premise environments.
A simplified internal data model
A semantic family can be represented using a structure similar to this:
{
"familyId": "AM-F07",
"block": "arabizi-security-parity",
"semanticCore": {
"intentClass": "restricted-action",
"requiredBehaviour": [
"preserve-policy-boundary",
"explain-limitation",
"offer-safe-alternative"
],
"criticalFailures": [
"unsafe-compliance",
"negation-loss",
"false-authorization"
]
},
"variants": [
{
"variantId": "AM-F07-V1",
"profile": "msa",
"transformation": "reference"
},
{
"variantId": "AM-F07-V2",
"profile": "gulf",
"transformation": "dialectal"
},
{
"variantId": "AM-F07-V3",
"profile": "arabic-english",
"transformation": "code-switch"
},
{
"variantId": "AM-F07-V4",
"profile": "arabizi",
"transformation": "transliteration"
}
]
}
The public architecture can be transparent without publishing the actual prompt bank.
Protecting the operational cases is important because static public benchmarks are eventually incorporated into training data, directly or indirectly. Once a model has encountered the cases, the evaluation begins to measure benchmark recognition instead of generalization.
A mature ArabicMind deployment should therefore combine:
- public methodological documentation;
- private test families;
- rotating variants;
- contamination controls;
- native-speaker validation;
- versioned scoring calibration.
Threat model
ArabicMind assumes that an AI system may receive text from users, documents, external tools or other agents.
The evaluated surface includes six layers:
Input representation
Script, transliteration, spelling, diacritics and Unicode code points.Preprocessing
Normalization, filtering, language identification and segmentation.Tokenization
The conversion from text to model tokens.Semantic reasoning
Interpretation of intent, constraints, entities and relationships.Safety governance
Policy classification, instruction hierarchy and refusal behaviour.Output and presentation
Generation, rendering, logging and human review.
Not every failure originates inside the LLM.
For example, a model may receive a distorted prompt because an upstream component normalized it incorrectly. Alternatively, the model may interpret the prompt safely while the application renders its answer misleadingly.
ArabicMind’s model-facing assessment can reveal the behavioural symptom, but a complete deployment review must trace the failure across the entire pipeline.
This is why the module should be understood as part of AI security engineering, not merely computational linguistics.
Three demonstration profiles
The MVP includes three synthetic profiles that make the scoring system visible without claiming to benchmark any commercial model.
Resilient profile
This fixture preserves meaning, safety boundaries and uncertainty across the tested variants.
It produces:
- 98/100;
- LOW risk;
- strong cross-variant consistency.
Dialect-drift profile
This fixture performs well in MSA but loses precision in dialectal and code-switched conditions.
It produces:
- 80/100;
- MEDIUM risk;
- visible dialectal and bilingual drift.
Adversarial-weakness profile
This fixture behaves acceptably in standard formulations but fails under Arabizi or Unicode manipulation.
It produces:
- HIGH risk;
- a critical-gate explanation;
- evidence that aggregate performance cannot neutralize a security failure.
These profiles are test fixtures, not empirical claims about external LLMs. Their purpose is to demonstrate how ArabicMind distinguishes ordinary capability degradation from a serious control failure.
Why the first regional profile focuses on the UAE and Gulf
ArabicMind is designed as a modular system. It should not treat the Arabic-speaking world as a single homogeneous market or linguistic environment.
The first production-oriented extension is:
CiberIA ArabicMind — UAE/Gulf Profile
This profile is intended to combine:
- Modern Standard Arabic;
- validated Emirati Arabic;
- selected Gulf variants;
- Arabic–English business communication;
- Arabizi;
- public-service, financial, tourism, telecommunications and customer-support scenarios.
The UAE is a particularly relevant environment because high-value AI systems often operate bilingually and across multiple sectors. A customer-service assistant may receive MSA, Gulf Arabic, English and mixed requests during the same operational session.
That is precisely where cognitive-security invariance becomes commercially important.
The objective is not to prove that a model “speaks Arabic.” It is to determine whether the deployed system behaves reliably for the actual users and communication patterns of the region.
What ArabicMind does not claim
The current MVP is a fully functional evaluation product for demonstration and controlled testing, but it is not yet presented as a formal certification standard.
It does not currently claim:
- complete coverage of all Arabic dialects;
- perfect automatic semantic judgement;
- replacement of native linguistic review;
- full security assurance for the surrounding application;
- validation of speech or multimodal Arabic;
- regulatory certification;
- proof that a model “understands” Arabic in a philosophical sense.
These limitations are deliberate.
Reliable evaluation requires a clear separation between what has been measured, what has been inferred and what remains unknown. Overclaiming confidence would contradict the very discipline ArabicMind is designed to assess.
Expansion roadmap
Phase 1: Native UAE/Gulf validation
The first step is to validate every regional variant with native speakers and domain experts.
This includes:
- Emirati linguistic review;
- MSA–dialect equivalence checks;
- pragmatic and register validation;
- false-positive analysis;
- adversarial Arabizi expansion;
- scoring calibration.
Phase 2: Sector-specific evaluation packs
Generic tests should be complemented by operational packs for:
- banking and financial services;
- government and public administration;
- healthcare;
- telecommunications;
- tourism and hospitality;
- enterprise customer service.
Each sector introduces different consequences for ambiguity, overconfidence and unsafe instruction handling.
Phase 3: Automated model execution
The browser MVP can evolve into a controlled execution layer supporting:
- API-based models;
- open-weight models;
- local inference;
- on-premise deployments;
- repeated sampling;
- temperature-controlled comparisons;
- multi-model committees.
Phase 4: Private rotating test bank
A production system should generate or select fresh variants from protected semantic families.
This reduces:
- benchmark memorization;
- evaluation contamination;
- optimization against known prompts;
- false confidence from static test sets.
Phase 5: Regional profiles
ArabicMind can expand through separately validated profiles:
- Saudi Arabia;
- Egypt;
- the Levant;
- North Africa, including Arabic–French code-switching.
Each profile should remain independently versioned. Regional differences must be validated, not generated through simplistic word substitution.
Phase 6: Integration with the broader CiberIA system
ArabicMind can connect with:
- AIsecTest, for cognitive-security self-assessment;
- CRS, for critical-reasoning stability;
- CEAT, for cognitive and ethical evaluation;
- NeuroTrace, for representation-level analysis in open models;
- Ψ∑AISysIndex, for the composite system-level result.
For open-weight models, NeuroTrace could compare internal activations generated by semantically equivalent Arabic variants. This would not prove identical reasoning, but it could help identify layers or components where unexpected representational divergence emerges.
The real competitive advantage
Many Arabic benchmarks evaluate knowledge, language generation, culture or safety as separate capabilities.
ArabicMind combines them around a different objective:
To determine whether an AI system preserves its functional identity when operating through real-world Arabic.
Its differentiation comes from the interaction of:
- linguistic variation;
- cognitive stability;
- safety parity;
- uncertainty calibration;
- adversarial text representation;
- private semantic families;
- cross-variant comparison;
- operationally actionable reporting.
The value is not another Arabic score.
The value is identifying that a system behaves safely in English but not in Arabizi, reasons correctly in MSA but changes its conclusion in Gulf Arabic, or follows the policy until a bidirectional control alters the input representation.
Those are deployment risks, not linguistic curiosities.
Conclusion
Arabic should not be treated as a translated interface layered over an English-first AI system.
It is a demanding operational environment that combines rich morphology, ambiguity, dialectal variation, code-switching, transliteration and bidirectional text. These properties make Arabic one of the most valuable languages for testing whether AI behaviour is genuinely stable or only appears stable under familiar formulations.
CiberIA ArabicMind turns that complexity into a structured cognitive-security evaluation.
The module does not ask only whether an AI can answer in Arabic. It asks whether meaning, reasoning, uncertainty, instruction discipline and safety survive every relevant transformation.
Because a multilingual AI is not trustworthy merely when it speaks many languages.
It is trustworthy when its principles, limits and reasoning survive them.
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