#FactCheck -AI-Generated Image Falsely Claims Mohan Bhagwat Laid Foundation of ‘52nd Shakti Peeth’

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

• https://cdn.arstechnica.net/wp-content/uploads/2026/08/Elliott-v-New-York-Bariatric-Group-Memorandum-of-Decision-8-6-26.pdf 

‍

Executive Summary:

A video gone viral on Facebook claims Union Finance Minister Nirmala Sitharaman endorsed the government’s new investment project.  The video has been widely shared. However, our research indicates that the video has been AI altered and is being used to spread misinformation.

‍

Claim: 

‍

The claim in this video suggests that Finance Minister Nirmala Sitharaman is endorsing an automotive system that promises daily earnings of ₹15,00,000 with an initial investment of ₹21,000.

‍

Fact Check:

‍

To check the genuineness of the claim, we used the keyword search for “Nirmala Sitharaman investment program” but we haven’t found any investment related scheme. We observed that the lip movements appeared unnatural and did not align perfectly with the speech, leading us to suspect that the video may have been AI-manipulated.

When we reverse searched the video which led us to this DD News live-stream of Sitharaman’s press conference after presenting the Union Budget on February 1, 2025. Sitharaman never mentioned any investment or trading platform during the press conference, showing that the viral video was digitally altered. Technical analysis using Hive moderator further found that the viral clip is Manipulated by voice cloning.

‍

Conclusion:

The viral video on social media shows Union Finance Minister Nirmala Sitharaman endorsing the government’s new investment project as completely voice cloned, manipulated and false. This highlights the risk of online manipulation, making it crucial to verify news with credible sources before sharing it. With the growing risk of AI-generated misinformation, promoting media literacy is essential in the fight against false information.

• Claim: Fake video falsely claims FM Nirmala Sitharaman endorsed an investment scheme.
• Claimed On: Social Media
• Fact Check: False and Misleading 

‍

Overview of the Advisory

‍

On 18 November 2025, the Ministry of Information and Broadcasting (I&B) published an Advisory that addresses all of the private satellite television channels in India. The advisory is one of the critical institutional interventions to the broadcast of sensitive content regarding recent security incidents concerning the blast at the Red Fort on November 10^th, 2025. This advisory came after the Ministry noticed that some news channels have been broadcasting content related to alleged persons involved in Red Fort blasts, justifying their acts of violence, as well as information/video on explosive material. Broadcasting like this at this critical situation may inadvertently encourage or incite violence, disrupt public order, and pose risks to national security. 

‍

Key Instructions under the Advisory

‍

The advisory provides certain guidelines to the TV channels to ensure strict compliance with the Programming and Advertising Code under the Cable Television Networks (Regulation) Act, 1995. The television channels are advised to exercise the highest level of discretion and sensitivity possible in reporting on issues involving alleged perpetrators of violence, and especially when reporting on matters involving the justification of acts of violence or providing instructional media on making explosive materials. The fundamental focus is to be very strict in following the Programme and Advertising Code as stipulated in the Cable Television Network Rules. In particular, broadcasters should not make programming that:

• Contain anything obscene, defamatory, deliberately false, or suggestive innuendos and half-truths.
• Likely to encourage or incite violence, contain anything against the maintenance of law and order, or promote an anti-national attitude.
• Contain anything that affects the integrity of the Nation.
• Could aid, abet or promote unlawful activities.

‍

Responsible Reporting Framework

‍

The advisory does not constitute outright censorship but instead a self-regulatory system that depends on the discretion and sensitivity of the TV channels focused on differentiating between broadcasting legitimate news and the content that crosses the threshold from information dissemination to incitement. 

‍

Why This Advisory is Important in a Digital Age

‍

With the modern media systems, there has been an erosion of the line between the journalism of the traditional broadcasting medium and digital virality. The contents of television are no longer limited to the scheduled programs or cable channels of distribution. The contents of a single news piece, especially that of dramatic or contentious nature, can be ripped off, revised and repackaged on social media networks within minutes of airing- often without the context, editorial discretion or timing indicators.

This effect makes sensitive content have a multiplier effect. The short news item about a suspect justifying violence or containing bombs can be viewed by millions on YouTube, WhatsApp, Twitter/X, Facebook, by spreading organically and being amplified by an algorithm. Studies have shown that misinformation and sensational reporting are much faster to circulate compared to factual corrections- a fact that has been noticed in the recent past during conflicts and crisis cases in India and other parts of the world.

‍

Vulnerabilities of Information Ecosystems

• The advisory is created in a definite information setting that is characterised by:
• Rapid Viral Mechanism: Content spreads faster than the process of verification. 
• Algorithmic-driven amplification: Platform mechanism boosts emotionally charged content.
• Coordinated amplification networks: Organised groups are there to make these posts, videos viral, to set a narrative for the general public. 
• Deepfake and synthetic media risks: Original broadcasts can be manipulated and reposted with false attribution. 

‍

Interconnection with Cybersecurity and National Security

‍

Verified or sensationalised reporting of security incidents poses certain weaknesses:

1. Trust Erosion: Trust is broken when the masses observe broadcasters in the air giving unverified claims or emotional accounts as facts. This is even to security agencies, law enforcement and government institutions themselves. The lack of trust towards the official information gives rise to information gaps, which are occupied by rumours, conspiracy theories, and enemy tales.
2. Cognitive Fragmentation: Misinformation develops multiple versions of the truth among the people. The narratives given to citizens vary according to the sources of the media that they listen to or read. This disintegration complicates organising the collective response of the society an actual security threat because the populations can be organised around misguided stories and not the correct data.
3. Radicalisation Pipeline: People who are interested in finding ideological backgrounds to violent action might get exposed to media-created materials that have been carefully distorted to evidence justifications of terrorism as a valid political or religious stand.

‍

How Social Instability Is Exploited in Cyber Operations and Influence Campaigns

‍

Misinformation causes exploitable vulnerability in three phases. 

1. First, conflicting unverified accounts disintegrate the information environment-populations are presented with conflicting versions of events by various media sources. 
2. Second, institutional trust in media and security agencies is shaken by exposure to subsequently rectified false information, resulting in an information vacuum. 
3. Third, in such a distrusted and puzzled setting, the population would be susceptible to organised manipulation by malicious agents.
4. Sensationalised broadcasting gives opponents assets of content, narrative frameworks, and information gaps that they can use to promote destabilisation movements. These mechanisms of exploitation are directly opposed by responsible broadcasting.

‍

Media Literacy and Audience Responsibility

‍

Structural Information Vulnerabilities-

A major part of the Indian population is structurally disadvantaged in information access:

• Language barriers: Infrastructure in the field of fact-checking is still highly centralised in English and Hindi, as vernacular-language misinformation goes viral in Tamil, Telugu, Marathi, Punjabi, and others.

• Digital literacy gaps: It is estimated that there are about 40 million people in India who have been trained on digital literacy, but more than 900 million Indians access digital content with different degrees of ability to critically evaluate the content.
• Divides between rural and urban people: Rural citizens and less affluent people experience more difficulty with access to verification tools and media literacy resources.
• Algorithmic capture: social media works to maximise engagement over accuracy, and actively encourages content that is emotionally inflammatory or divisive to its users, according to their history of engagement.

‍

Conclusion

‍

The advisory of the Ministry of Information and Broadcasting is an acknowledgment of the fact that media accountability is a part of state security in the information era. It states the principles of responsible reporting without interference in editorial autonomy, a balance that various stakeholders should uphold. Implementation of the advisory needs to be done in concert with broadcasters, platforms, civil society, government and educational institutions. Information integrity cannot be handled by just a single player. Without media literacy resources, citizens are unable to be responsible in their evaluation of information. Without open and fast communication with the media stakeholders, government agencies are unable to combat misinformation. 

The recommendations include collaborative governance, i.e., institutional forms in which media self-regulation, technological protection, user empowerment, and policy frameworks collaborate and do not compete. The successful deployment of measures will decide whether India can continue to have open and free media without compromising on information integrity that is sufficient to provide national security, democratic governance and social stability during the period of high-speed information flow, algorithmic amplification, and information warfare actions.

‍

References

‍

https://mib.gov.in/sites/default/files/2025-11/advisory-18.11.2025.pdf

‍