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Battle of transformers: Adversarial attacks on financial sentiment models
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DOI：10.1016/j.jbankfin.2026.107698.png)
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Abstract
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Financial sentiment analysis models increasingly drive automated trading and risk assessment, yet their vulnerability to adversarial manipulation remains poorly understood. We demonstrate that subtle, human-imperceptible textual changes can systematically fool leading sentiment classifiers. Using GPT-4o to generate semantically equivalent paraphrases, optimized via embedding similarity ratios, we attack FinBERT and FinGPT across three financial datasets. Our method alters predictions in 20%–54% of cases, reducing accuracy by 10–26 percentage points. We identify three critical vulnerabilities: difficulty with numbers lacking directional cues, misinterpretation of double negatives, and oversensitivity to trigger words. These findings reveal security risks in automated financial systems and underscore the need for more robust models.
Keywords:
G14
G17
C63
C45
Adversarial attacks
Large language models
Financial sentiment analysis
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