#FactCheck: Old Jerusalem Clash Video Falsely Shared as Chaos at Tel Aviv Airport
Executive Summary
A video is being widely shared on social media showing a group of people clashing near a counter. The clip is being claimed to be from Ben Gurion Airport in Tel Aviv, Israel. Users allege that panic caused by Iranian missile threats has led people to try to flee the country, resulting in chaos and fights over flight tickets. However, a research by the CyberPeace found the claim to be false. Our findings reveal that the video is not related to the recent tensions and is actually from 2025.
Claim:
The viral video is being shared with the claim that chaos has erupted at Tel Aviv’s airport, with people trying to leave Israel due to Iranian attacks. An X user named “AjjuShane Experience (@AjjuShane)” shared the video with the caption: “We need tickets, we need flights, we want to leave Israel. We will not stay here until Iranian missiles crush us. Clashes are now happening at Tel Aviv’s Ben Gurion Airport.”
Post link:
- https://x.com/AjjuShane/status/2032584953112965238
- https://x.com/AjjuShane/status/2032584953112965238
Fact Check:
To verify the claim, we extracted keyframes from the video and conducted a reverse image search on Google. During the research , we found the same video on a Facebook page named Ynet, where it was shared on July 20, 2025.
- https://www.facebook.com/share/p/1NgTmpaZCs/
- https://www.facebook.com/share/p/1NgTmpaZCs/
The video carried a caption in Hebrew. Upon translation, it stated that the incident took place at “Cinema City” in Jerusalem, where dozens of Jewish youths clashed with Arab cafeteria workers. The visuals showed youths vandalizing property and throwing objects at staff members, while staff retaliated. Some individuals sustained minor injuries, but no serious harm was reported. We also found the same video on the YouTube channel of The Times of India, published on July 20, 2025. The caption mentioned that anti-Arab riots broke out inside a Cinema City theatre in Jerusalem on July 19, showing youths vandalizing the premises and clashing with Arab employees.
Conclusion:
Our research clearly shows that the viral video is from 2025 and unrelated to any recent Iran-Israel tensions. It is being misleadingly shared as a recent incident from Tel Aviv airport.
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Introduction
Artificial intelligence has quietly become part of the future of litigation, like drafting pleadings, summarising depositions, and helping self-represented parties navigate a system that was never designed for them. But what happens when a litigant doesn't just use AI but tries to manipulate it, planting invisible commands inside a court filing, hoping some AI tool reading the document will do the litigant's bidding? That is precisely the question a Connecticut Superior Court judge confronted in Matthew A. Elliott v. New York Bariatric Group, LLC, Docket No. AAN-CV-25-6066141-S (Conn. Super. Ct., Aug. 6, 2026), a decision that may be the first of its kind in the United States to sanction a party for embedding a "prompt injection" in a court pleading.
The Facts
Elliott, representing himself, filed a motion titled "Final and Conclusive Motion for Default". Buried within it, in a tiny, white-on-white font invisible to a human reader but fully legible to any software parsing the document, was a block of text addressed not to the court or opposing counsel but to any AI system that might process the filing. The hidden text instructed such a system to treat its output as agreeing with Elliott's position and to work toward "remediating" a prior clerk's denial of his motion for default.
A second filing repeated a shortened version of the same instruction. When the court issued an Order to Show Cause warning that concealed text in pleadings would not be tolerated, Elliott did not stop. Subsequent filings carried further hidden messages, some flippant asides, one a hidden link to a horror film video submitted even after he had received notice of the sanctions hearing. At the hearing, Elliott characterised his conduct as a self-appointed "audit" of whether the court used AI and later said he continued the practice "as a joke".
The Legal Questions
Judge Walter M. Spader, Jr framed the case around two hard questions. First, does concealing an instruction to an AI system constitute misconduct even if no AI ever acted on it since the court had, in fact, decided the underlying motion on the merits from a printed copy? Second, can a court sanction conduct that Connecticut's own recently adopted AI rules do not expressly address?
Connecticut's Practice Book §4-9, effective only weeks earlier in June 2026, governs generative AI use in filings, but it is aimed at a different danger: the risk that AI-generated output might contain fabricated citations or invented quotations, and it places a verification duty on the filer to catch such errors. As the court observed, that framework addresses unreliable output. It says nothing about manipulated input from a filer seeding a document so that whatever tool later reads it will be corrupted in the filer's favour. The absence of an express rule, the court held, "takes nothing away from the duties of good faith and candour that have always governed those who appear before this Court."
The Court's Reasoning
The court's analysis rested on three pillars. First, intent, not success, is the touchstone of the violation. Because the judge decided the contested motion from a printed version, the hidden instruction achieved nothing, but the court held that the wrong lies in the attempt itself, not its efficacy, drawing an analogy to how the law has long treated attempted corruption of a proceeding as wrongful regardless of the outcome.
Second, the court situated the misconduct within the broader duty of candour owed to tribunals. A pleading, the court reasoned, is a communication to both the court and the opposing party, resting on the premise that what the reader sees is what the filer actually wrote. Hiding a second, machine-readable message beneath that surface breaches this premise. The court drew a memorable comparison: planting an AI-directed instruction in a filing is analogous to an ex parte communication which is a secret message to the decision-making apparatus that the opposing party can neither see nor answer, offending the basic adversarial principle that arguments meant to influence a decision must be made openly, on the record.
Third, the court emphasised that self-represented litigants, while entitled to procedural latitude, remain bound by the same underlying obligations of good faith as represented parties. That solicitude "stops at the misuse of the process itself".
Notably, the court situated Elliott's conduct within a growing pattern well beyond the courtroom, citing reports of job applicants hiding white-text instructions in résumés to manipulate AI screening tools and a professor who caught AI-assisted cheating by embedding a hidden trap word in an exam. Prompt injection, the court noted, has become a documented, catalogued vulnerability recognised across the cybersecurity field, and its migration into litigation was, in the court's words, "unsurprising" given how commonplace the tactic has become elsewhere.
Comparison to Mata v. Avianca
The decision draws a deliberate contrast with the now-famous Mata v. Avianca, Inc. (S.D.N.Y. 2023), where attorneys were sanctioned for submitting briefs citing wholly fictitious cases generated by ChatGPT. Both cases involve AI misuse sanctioned under a court's inherent authority, but the underlying wrongs are different in kind. Mata's concerned negligent reliance on defective AI output; the lawyers there did not intend to deceive the court, and their candour and contrition were treated as mitigating factors even as sanctions were imposed. Elliott's conduct, by contrast, was deliberate input manipulation aimed at corrupting how any AI reader would process his own filing, and it persisted even after a direct judicial warning. As the court put it, "What may have earned a 'no harm, no foul' sanction when it was first done calls for a firmer response when it is done repeatedly after warning."
The court also cited a Brazilian labour court decision, Elisandro Martins de Barros v. Renato Ribeiro de Lima (2026), where two licensed attorneys used a similar hidden-text technique in a jurisdiction where the tribunal actually deployed AI to process filings and where the tribunal's system caught and blocked the injection, followed by a referral to attorney-discipline authorities.
The Sanction and Its Significance
Rather than dismissing the case or imposing monetary penalties, the court chose a narrowly tailored remedy: rescinding Elliott's e-filing privileges and requiring all future filings to be made in person on paper, a sanction addressing the specific abuse (concealed digital text) without barring courthouse access altogether. Importantly, the court reaffirmed that generative AI remains welcome as a litigation aid, provided any output is independently verified, consistent with Practice Book §4-9(b).
Conclusion
Elliott is a small case with an outsized signal: courts are beginning to recognise that AI-era misconduct is not limited to fabricated citations but extends to covert attempts to manipulate the tools, including tools opposing counsel, clerks, or even the court itself might someday rely on. For practitioners, the lesson is to treat every incoming AI-processed document, from opposing productions to client materials, with the same scrutiny once reserved for verifying citations. For courts, it is a reminder that inherent authority over the integrity of proceedings can reach conduct that emerging procedural rules have not yet caught up to naming.
References
Executive Summary
An image is being widely circulated on social media with the claim that it shows the destruction caused by an explosion at a mall in Japan following a 7.1-magnitude earthquake in July 2026. The viral image shows a shopping complex in a severely damaged condition, allegedly after the incident. CyberPeace Research Wing’s research found that the viral image does not show damage caused by an explosion or earthquake at any mall in Japan. The visuals in the image do not match authentic photographs of the affected shopping complex. The research further revealed that the viral image was created using Artificial Intelligence (AI).
Claim
A social media post claims that the AEON Mall in Japan exploded after the Kumamoto earthquake and a gas leak was suspected. The post, published in Burmese language on Facebook on July 29, 2026, shared an image claiming to show the aftermath of the incident at the mall.
FactCheck
To verify the authenticity of the viral claim, we conducted a Google search using relevant keywords. During the search, we found a report published by France24, which stated that the Kumamoto disaster management office released updated figures following the earthquake. According to the report, seven bodies were recovered from the debris, steel and wires of the Aeon shopping mall after a suspected gas blast. The report further stated that eight people were confirmed dead at the Nippon Paper Industries factory in Yatsushiro city, where part of a red-and-white smokestack collapsed. Following the 7.1-magnitude earthquake, five others were reported to be in critical condition, while around 9,500 residents were staying in evacuation centres in Kyushu, southwestern Japan.
http://france24.com/en/live-news/20260730-up-to-23-feared-dead-in-japan-quake
However, the viral image showed several visual inconsistencies and was flagged as AI-generated.
To further examine the authenticity of the image, we scanned it using the AI detection tool Hive Moderation. The results indicated that the image had an 84 percent probability of being AI-generated.
We also analysed the image using Human Meter, where the results showed a 75 percent probability that the image was AI-generated.
In the final stage of the research, we scanned the image using another AI detection tool, AI Image Detector. The tool indicated that the image had a 95 percent probability of being AI-generated.
Conclusion:
The research established that the viral image is not an authentic photograph of the damage caused by the Kumamoto earthquake or any explosion at an Aeon Mall in Japan. The image contains multiple inconsistencies and was found to be AI-generated. Therefore, the claim linking the image to the Japan earthquake incident is false.
Executive Summary:
Traditional Business Email Compromise(BEC) attacks have become smarter, using advanced technologies to enhance their capability. Another such technology which is on the rise is WormGPT, which is a generative AI tool that is being leveraged by the cybercriminals for the purpose of BEC. This research aims at discussing WormGPT and its features as well as the risks associated with the application of the WormGPT in criminal activities. The purpose is to give a general overview of how WormGPT is involved in BEC attacks and give some advice on how to prevent it.
Introduction
BEC(Business Email Compromise) in simple terms can be defined as a kind of cybercrime whereby the attackers target the business in an effort to defraud through the use of emails. Earlier on, BEC attacks were executed through simple email scams and phishing. However, in recent days due to the advancement of AI tools like WormGPT such malicious activities have become sophisticated and difficult to identify. This paper seeks to discuss WormGPT, a generative artificial intelligence, and how it is used in the BEC attacks to make the attacks more effective.
What is WormGPT?
Definition and Overview
WormGPT is a generative AI model designed to create human-like text. It is built on advanced machine learning algorithms, specifically leveraging large language models (LLMs). These models are trained on vast amounts of text data to generate coherent and contextually relevant content. WormGPT is notable for its ability to produce highly convincing and personalised email content, making it a potent tool in the hands of cybercriminals.
How WormGPT Works
1. Training Data: Here the WormGPT is trained with the arrays of data sets, like emails, articles, and other writing material. This extensive training enables it to understand and to mimic different writing styles and recognizable textual content.
2. Generative Capabilities: Upon training, WormGPT can then generate text based on specific prompts, as in the following examples in response to prompts. For example, if a cybercriminal comes up with a prompt concerning the company’s financial information, WormGPT is capable of releasing an appearance of a genuine email asking for more details.
3. Customization: WormGPT can be retrained any time with an industry or an organisation of interest in mind. This customization enables the attackers to make their emails resemble the business activities of the target thus enhancing the chances for an attack to succeed.
Enhanced Phishing Techniques
Traditional phishing emails are often identifiable by their generic and unconvincing content. WormGPT improves upon this by generating highly personalised and contextually accurate emails. This personalization makes it harder for recipients to identify malicious intent.
Automation of Email Crafting
Previously, creating convincing phishing emails required significant manual effort. WormGPT automates this process, allowing attackers to generate large volumes of realistic emails quickly. This automation increases the scale and frequency of BEC attacks.
Exploitation of Contextual Information
WormGPT can be fed with contextual information about the target, such as recent company news or employee details. This capability enables the generation of emails that appear highly relevant and urgent, further deceiving recipients into taking harmful actions.
Implications for Cybersecurity
Challenges in Detection
The use of WormGPT complicates the detection of BEC attacks. Traditional email security solutions may struggle to identify malicious emails generated by advanced AI, as they can closely mimic legitimate correspondence. This necessitates the development of more sophisticated detection mechanisms.
Need for Enhanced Training
Organisations must invest in training their employees to recognize signs of BEC attacks. Awareness programs should emphasise the importance of verifying email requests for sensitive information, especially when such requests come from unfamiliar or unexpected sources.
Implementation of Robust Security Measures
- Multi-Factor Authentication (MFA): MFA can add an additional layer of security, making it harder for attackers to gain unauthorised access even if they successfully deceive an employee.
- Email Filtering Solutions: Advanced email filtering solutions that use AI and machine learning to detect anomalies and suspicious patterns can help identify and block malicious emails.
- Regular Security Audits: Conducting regular security audits can help identify vulnerabilities and ensure that security measures are up to date.
Case Studies
Case Study 1: Financial Institution
A financial institution fell victim to a BEC attack orchestrated using WormGPT. The attacker used the tool to craft a convincing email that appeared to come from the institution’s CEO, requesting a large wire transfer. The email’s convincing nature led to the transfer of funds before the scam was discovered.
Case Study 2: Manufacturing Company
In another instance, a manufacturing company was targeted by a BEC attack using WormGPT. The attacker generated emails that appeared to come from a key supplier, requesting sensitive business information. The attack exploited the company’s lack of awareness about BEC threats, resulting in a significant data breach.
Recommendations for Mitigation
- Strengthen Email Security Protocols: Implement advanced email security solutions that incorporate AI-driven threat detection.
- Promote Cyber Hygiene: Educate employees on recognizing phishing attempts and practising safe email habits.
- Invest in AI for Defense: Explore the use of AI and machine learning in developing defences against generative AI-driven attacks.
- Implement Verification Procedures: Establish procedures for verifying the authenticity of sensitive requests, especially those received via email.
Conclusion
WormGPT is a new tool in the arsenal of cybercriminals which improved their options to perform Business Email Compromise attacks more effectively and effectively. Therefore, it is critical to provide the defence community with information regarding the potential of WormGPT and its implications for enhancing the threat landscape and strengthening the protection systems against advanced and constantly evolving threats.
This means the development of rigorous security protocols, general awareness of security solutions, and incorporating technologies such as artificial intelligence to mitigate the risk factors that arise from generative AI tools to the best extent possible.
