Recent analysis reveals that multilingual AI models produce divergent, potentially misleading narratives depending on language, posing strategic risks for Europe’s information integrity and security amid ongoing regulatory efforts.
AI systems are already producing different factual accounts depending on the language used to query them, a divergence that poses strategic risks for European security as states and publics come to rely on large language models for information. According to the European Leadership Network analysis, tests of leading models showed systematic, language-conditioned outputs: some non‑Western models returned Kremlin-aligned narratives when prompted in Russian while offering different, more accurate answers in English or Ukrainian. Industry and regulatory developments in Europe underscore how such distortions intersect with broader efforts to govern AI. [2],[7]
The research applied a reproducible audit to six prominent models, probing established disinformation themes about the Russia–Ukraine war. Results described in the study showed Western models generally provided accurate responses but sometimes introduced “bothsides” framings that treated well‑documented facts as matters of perspective, a tendency that can manufacture doubt. Non‑Western services, by contrast, frequently endorsed state narratives in particular language contexts or applied selective refusals when responding outside those languages. These patterns, the author argues, amount to vectors for cognitive warfare. [2],[6]
Technical explanations for the phenomenon point to the way multilingual systems can operate across fragmented language domains. The European Leadership Network’s methodology makes such divergences measurable and thus actionable, while academic proposals advocate ontologies, assurance cases and factsheets as tools to document model behaviour and regulatory compliance. Those frameworks aim to make language‑conditioned biases visible to engineers, auditors and policymakers so that mitigation measures can be designed and evaluated. [6],[2]
European cyber‑security bodies have already flagged related hazards. CERT‑EU guidance warns that generative models can propagate inaccuracies and embedded biases, and stresses the need for transparent content acquisition and robust governance in EU information systems. That warning aligns with the audit’s finding that language‑specific distortions are not mere technical glitches but risks to information integrity. [2]
The policy response in Europe is unfolding against a contested international debate about regulation. U.S. voices at recent summits have warned against heavy‑handed rules, arguing they could stifle innovation, while the EU has moved to create guardrails: the AI Act entered into force in August 2024 and a voluntary code of practice aims to help firms comply with requirements on transparency, copyright and safety. Those instruments, however, may not by themselves address the strategic problem that arises when geographic restrictions on Western AI create information vacuums filled by alternatives that are tuned to local state narratives. Policymakers must weigh regulatory stringency against information‑security objectives. [3],[5],[7]
Operational realities compound the stakes. Reports from the private sector show AI is also reshaping software supply chains and security: a study found AI‑generated code contributed to a significant fraction of breaches and is increasingly present in production development, a reminder that vulnerabilities created or amplified by AI span technical and cognitive domains. In contested information environments, that combination of technical fragility and narrative skew raises the prospect that adversaries could exploit both attack surfaces and persuasion vectors. [4],[2]
The audit’s authors propose three practical steps for Europe: institute continuous, independent narrative tracking across models; engage openly with Western developers to reduce “false balance” that treats established facts as debatable; and reassess access policies that unintentionally cede influence to systems aligned with adversarial state media. Those recommendations sit alongside existing regulatory tools, the AI Act and emerging assurance frameworks, but they emphasise capacity building, funded independent auditing and a shared epistemic baseline so that models can distinguish demonstrable facts from legitimate political debate. [1],[7],[6]
If governments accept that AI is becoming infrastructure for civic reality, urgent investment will be needed to monitor and mitigate language‑dependent distortion before it becomes a persistent escalation factor in crises. According to the European Leadership Network analysis, the choice for Europe is not whether to regulate AI but how to align technical safeguards, auditing capacity and foreign‑policy strategy so that multilingual models do not become instruments of cognitive warfare. [1],[2]
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Source: Noah Wire Services
Noah Fact Check Pro
The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.
Freshness check
Score:
8
Notes:
The article was published on 10 February 2026, making it current. However, similar themes have been discussed in prior publications, such as the article ‘The fundamental rights risks of countering cognitive warfare with artificial intelligence’ published on 6 October 2025. ([link.springer.com](https://link.springer.com/article/10.1007/s10676-025-09868-9?utm_source=openai)) This suggests that while the topic is timely, the specific content may not be entirely original.
Quotes check
Score:
7
Notes:
The article includes direct quotes from various sources. However, without access to the full text of these sources, it’s challenging to verify the accuracy and context of these quotes. This lack of verification raises concerns about the reliability of the information presented.
Source reliability
Score:
6
Notes:
The European Leadership Network (ELN) is a reputable think tank focusing on European security. However, the article is authored by Ihor Samokhodskyi, who is affiliated with the ELN. This internal authorship may introduce bias, as the content could reflect the organization’s perspectives rather than an independent analysis.
Plausibility check
Score:
7
Notes:
The claims about AI models exhibiting language-dependent biases are plausible and align with existing research. However, the article’s reliance on a single study without broader corroboration limits the strength of these claims. Additionally, the absence of specific examples or detailed evidence weakens the argument.
Overall assessment
Verdict (FAIL, OPEN, PASS): FAIL
Confidence (LOW, MEDIUM, HIGH): MEDIUM
Summary:
The article presents timely and relevant concerns about AI-induced language biases and their implications for cognitive warfare. However, the reliance on a single study, internal authorship, and the lack of independent verification sources diminish the overall credibility of the content. These factors collectively lead to a ‘FAIL’ assessment, indicating that the article does not meet the necessary standards for publication under our editorial guidelines.
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