Can AI Spot the Phish? – Minority Mindset

Author: Denis Avetisyan

A new study rigorously assesses how well artificial intelligence, particularly large language models, can identify and flag malicious phishing attempts.

The evaluation experiment provides a comprehensive framework for assessing performance across a defined parameter space, allowing for systematic analysis and robust validation of the proposed methodology.

Research demonstrates that large language models outperform traditional methods for phishing detection, with screenshot analysis and temperature scaling proving crucial for optimal performance.

Despite advancements in cybersecurity, phishing attacks remain a persistent threat due to their increasing sophistication and ability to evade traditional detection methods. This research, ‘How Can We Effectively Use LLMs for Phishing Detection?: Evaluating the Effectiveness of Large Language Model-based Phishing Detection Models’, comprehensively evaluates the potential of large language models (LLMs) for identifying phishing websites and accurately determining targeted brands. Our findings demonstrate that while commercial LLMs generally outperform deep learning and open-source alternatives, optimal performance hinges on utilizing screenshot inputs with low temperature settings. How can these insights be leveraged to build more robust and adaptable phishing defenses in an evolving threat landscape?

Deconstructing the Phish: Adapting to an Evolving Threat

Phishing attacks are no longer the easily-spotted, grammatically-incorrect emails of the past; they represent a continuously adapting threat landscape for both individuals and organizations. Contemporary phishing campaigns increasingly leverage sophisticated techniques, including business email compromise, spear phishing tailored to specific individuals, and the exploitation of emerging technologies like AI to create convincingly realistic and personalized lures. These attacks move beyond simple mass distribution, targeting vulnerabilities in human psychology and increasingly bypassing traditional security filters. The financial and reputational damage resulting from successful phishing attempts continues to rise, with data breaches, ransomware infections, and identity theft becoming increasingly common consequences. This constant evolution necessitates a proactive and adaptive security posture, as reliance on signature-based detection alone is no longer sufficient to mitigate the risk posed by these persistent and inventive malicious actors.

Conventional phishing detection, reliant on blacklists of known malicious URLs and email senders, is increasingly ineffective against modern attacks. Sophisticated phishing campaigns now employ techniques like URL shortening, domain spoofing with subtle misspellings, and the use of compromised legitimate accounts to bypass these filters. Furthermore, attackers leverage dynamic content and personalized messaging, making it difficult to identify patterns based on static signatures. Machine learning models, while promising, require constant retraining to adapt to the ever-changing tactics employed by threat actors, and are often evaded through adversarial techniques designed to fool the algorithms. This arms race between security professionals and attackers necessitates a shift towards behavioral analysis and a more holistic approach to threat detection, moving beyond simply identifying what a threat is to understanding how it operates.

The swift and precise identification of phishing attempts represents a critical defense against escalating cyber threats. Delays or inaccuracies in detection can quickly lead to substantial data breaches, compromising sensitive personal and financial information. Organizations and individuals alike face significant financial repercussions from these breaches, including remediation costs, legal fees, and loss of customer trust. Moreover, the damage extends beyond immediate monetary losses, impacting reputation and long-term viability. Therefore, investment in robust detection systems and employee training focused on recognizing evolving phishing tactics isn’t merely a preventative measure—it’s a fundamental component of modern risk management and a safeguard against potentially devastating consequences.

The Impersonation Game: Why Brand Identification Matters

Accurate brand identification is fundamental to phishing detection because successful attacks rely on mimicking trusted entities. Determining the legitimate brand being impersonated allows security systems and analysts to move beyond generic phishing indicators and focus on brand-specific characteristics, such as visual branding, common login page elements, and typical communication styles. This targeted approach significantly improves detection rates and reduces false positives, as it enables differentiation between legitimate communications from the brand and malicious imitations. Without correct brand identification, analysis remains generalized and less effective at identifying subtle, yet critical, indicators of compromise.

Accurate identification of the targeted brand in a phishing attempt enables a more focused analytical approach to detection. By narrowing the scope to a specific entity, security systems can prioritize relevant indicators of compromise, such as known domain patterns, legitimate visual assets, and typical communication styles associated with that brand. This focused analysis reduces false positives and improves the efficiency of detection algorithms, ultimately leading to a higher rate of successful phishing identification. Furthermore, understanding the targeted brand facilitates the creation of more effective threat intelligence and proactive security measures tailored to specific organizational risks.

Analysis conducted during our study indicates that Gemini achieves a 94.59% accuracy rate in brand identification when presented with input consisting of both screenshots and associated URLs. This performance metric was determined through rigorous testing against a comprehensive dataset of phishing examples and legitimate brand assets. The methodology employed prioritized precise matching of visual elements within the screenshot alongside URL-based domain verification to minimize false positives and maximize accurate brand attribution. This result positions Gemini as the leading model in accurately discerning the targeted brand in potential phishing attempts, as compared to other evaluated models.

The Tightrope Walk: Balancing Precision and Reliability

The performance of phishing detection systems is fundamentally assessed by two error types: false positives and false negatives. A false positive incorrectly identifies legitimate email as phishing, leading to user inconvenience and potential distrust in the security system. Conversely, a false negative fails to identify actual phishing attempts, leaving users vulnerable to attacks and data compromise. Therefore, a balanced evaluation requires minimizing both rates, as prioritizing one over the other can significantly impact user experience and security posture. The optimal balance depends on the specific application and associated risk tolerance, but both metrics are critical components of a comprehensive performance analysis.

A high false positive rate in phishing detection—incorrectly flagging legitimate emails as malicious—negatively impacts user experience by creating unnecessary disruption and eroding trust in security systems. Conversely, a high false negative rate—failing to identify actual phishing attempts—directly increases the risk of successful attacks, potentially leading to data breaches, financial loss, and compromised accounts. The severity of these outcomes underscores the importance of minimizing both error types, though the consequences of a false negative are generally considered more critical due to the direct exposure to security threats.

Evaluation using the APWG eCX Dataset demonstrates that commercial Large Language Models (LLMs), specifically GPT, attain 93.86% accuracy in phishing detection. This translates to a false negative rate of only 0.95%. In comparison, deep learning models tested on the same dataset exhibited significantly higher false negative rates, consistently exceeding 64%. These results indicate a substantial performance difference, suggesting LLMs currently outperform deep learning approaches in identifying phishing attempts based on this dataset and metric.

The research demonstrates a pragmatic dismantling of assumptions regarding phishing detection, aligning with a core tenet of systems understanding. It isn’t enough to simply use a model; one must probe its limits, particularly concerning multimodal input and temperature scaling to truly gauge its efficacy. As Linus Torvalds once stated, “Talk is cheap. Show me the code.” This sentiment perfectly encapsulates the study’s approach – moving beyond theoretical claims to a rigorous, empirical evaluation of LLM performance. The findings—that screenshot inputs combined with lower temperature settings outperform traditional methods—aren’t merely improvements, but revelations born from actively breaking the expected norms of model behavior and observing the resulting strengths.

Deconstructing the Deception

The demonstrated efficacy of large language models in discerning malicious intent is not, ultimately, a solution, but a refinement of the problem. This research establishes that LLMs can identify phishing attempts, often exceeding the performance of established deep learning architectures. However, it simultaneously illuminates the inherent limitations of relying on pattern recognition – even highly sophisticated pattern recognition. The adversary doesn’t seek to improve the detection algorithm; the adversary adapts the attack vector. The models are, at best, tracing the shadow of the threat, not neutralizing its source.

Future work must move beyond simply improving detection rates. The emphasis should shift towards understanding why these models succeed, and more importantly, where they predictably fail. A deeper investigation into the adversarial space – deliberately crafting attacks designed to bypass these LLMs – will reveal the fault lines in their reasoning. Temperature scaling, while demonstrably effective, feels like adjusting the sensitivity of a sensor, not dismantling the threat. True progress lies in reverse-engineering the cognitive vulnerabilities these attacks exploit.

The integration of multimodal inputs – screenshots, in this instance – represents a logical progression, but it also introduces new layers of complexity. The model isn’t merely processing text; it’s interpreting visual cues, potentially opening avenues for adversarial manipulation via subtle image distortions. The objective, then, isn’t to build a perfect detector, but to build a system capable of continually deconstructing and re-evaluating its own assumptions about deception.

Original article: https://arxiv.org/pdf/2511.09606.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

Deconstructing the Phish: Adapting to an Evolving Threat

The Impersonation Game: Why Brand Identification Matters

The Tightrope Walk: Balancing Precision and Reliability

Deconstructing the Deception

See also: