#FactCheck - Old Wedding Fire Video Misleadingly Shared as Iranian Hypersonic Missile Strike in Tel Aviv
Executive Summary:
Amid the ongoing conflict involving the United States, Israel, and Iran, a video showing a building engulfed in flames is being widely circulated on social media. In the clip, a large fire can be seen inside a building while several people appear to be running in panic. The video is being shared with the claim that Iran fired a hypersonic missile targeting a ceremony in Tel Aviv, Israel, allegedly killing several Israeli military generals and other prominent figures.
However, research by the CyberPeace found that the claim is false. The video being circulated as footage of an attack in Israel actually predates the current conflict and shows a fire that broke out during a wedding ceremony.
Claim
A Facebook user named “Syed Asif Raza Jafri” shared the video on March 13, 2026, claiming that an Iranian hypersonic missile had struck a grand ceremony in Tel Aviv, where several Israeli military officers, generals, soldiers, and other important personalities were present. According to the post, the attack resulted in multiple casualties.
Source:
- https://www.facebook.com/reel/902182825912364
- https://ghostarchive.org/archive/rZryr
Fact Check
To verify the claim, we began our research using the Google Lens reverse image search tool. Several key frames from the viral video were extracted and searched online.
During the search, we found the same video shared earlier on multiple foreign social media accounts. A Facebook user named “Es de Bombero” from Chile had posted the video on January 17, 2026, describing it in Spanish as footage of a fire that broke out during a wedding celebration.
Our research shows that the viral video had been circulating on social media since at least January 15, 2026, well before the escalation of the current conflict. According to a report published on March 1, 2026, by BBC, the large-scale attacks on Iran by the United States and Israel began on February 28, 2026, after which Iran’s Supreme Leader Ali Khamenei was reported dead.
Additionally, a March 12, 2026 report by Al Jazeera stated that a house near Tel Aviv in central Israel was damaged by a rocket reportedly fired by Hezbollah, which has previously carried out joint attacks in coordination with Iran.
Conclusion
The viral video being shared as footage of an Iranian hypersonic missile strike in Tel Aviv is misleading. The clip is an older video of a fire that reportedly broke out during a wedding ceremony and was circulating online before the current conflict began.
While the exact location of the incident shown in the video cannot be independently verified, it is clear that the footage has no connection to the ongoing war between the United States, Israel, and Iran.
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Introduction
Since February 2020 the government has been taking keen steps to safeguard the Indian markets and the consumer, this could be seen in the forms of policies and exemptions for the market players and the consumers, however, due to the COVID-19 pandemic, the markets places became vulnerable to loss and various forms of new crimes and frauds. The Government recently tabled the Jan Vishwas bill which is an aftermath of the Vivad se Vishwas Bill, 2020 which was tabled in February 2020 for creating a safe and dynamic market, this bill is a clear example of how AtmaNirbhar Bharat plays a crucial role in nations development.
What is Jan Vishwas Bill, 2022
The Jan Vishwas (Amendment of Provisions) Bill, 2022 is a 108-page bill introduced in the Lok Sabha by the Union Minister of Commerce and Industry, Piyush Goyal. The statement of objects and reasons of the Bill states, “To amend certain enactments for decriminalizing and rationalizing minor offenses to further enhance trust-based governance for ease of living and doing business.” The bill aims to promote ease of doing business in India by decriminalizing minor offences and amending 183 provisions in 42 Acts administered by 19 ministries. The bill proposes to replace minor offences with monetary penalties and rationalize existing monetary penalties based on the gravity of the offences. The Acts to be amended by the bill include-
- Drugs and Cosmetics Act, 1940
- Public Debt Act, 1944
- Pharmacy Act, 1948
- Cinematograph Act, 1952
- Copyright Act, 1957
- Patents Act, 1970
- Environment (Protection) Act, 1986
- Motor Vehicles Act, 1988
- Trade Marks Act, 1999l Railways Act, 1989
- Information Technology Act, 2000
- Prevention of Money-laundering Act, 2002
- Food Safety and Standards Act, 2006
- Legal Metrology Act, 2009
- Factoring Regulation Act, 2011
The bill aims to decriminalize a large number of minor offences and replace them with monetary penalties. This step by the government is a clear indication of how important the market regulations are, in recent times Google was imposed with a penalty of 1300 crores and 900 crores for violating competitive market practices, these penalties, and criminalised actions will ensure proper compliance to laws of the land thus creating a blanket of safeguards for the Indian consumer and netizen.
What will the Ease of Business be?
The Government has been critical in pinpointing various parameters and factors to improve the ease of business in the country, this bill comes at the right time when we can see numerous start-ups and entrepreneurs emerging in our country. The parameters are as follows-
- Starting a Business of all
- Dealing with Construction Permits
- Getting Electricity
- Registering Property
- Getting Credit
- Protecting
- Minority Investors
- Paying Taxes
- Trading across Borders
- Enforcing Contracts and Resolving Insolvency
These parameters have been created with a sight on the future of the markets and how external factors like the Russia-Ukraine war can influence the markets. According to Minister Piyush Goyal, the fear of imprisonment for minor offences is a major factor hindering the growth of the business ecosystem and individual confidence in India. The Jan Vishwas Bill, 2022 aims to address this issue by replacing minor offences with monetary penalties. The bill also proposes an increase of 10% in the minimum amount of fine and penalty levied after every three years, once the bill becomes a law.
Conclusion
The bill will create a level playing field for the market players and the consumers with the backing of strong legislation and precedents thus maintaining transparency and accountability in the system. The amended provisions will allow various already existing legislation to come in tune with the current times and emerging technologies. The nation is at a critical juncture to fabricate policies and laws to address the issues and threats of the future and hence such a bill will be the strengthening pillar of the Indian markets and cyber-ecosystem. The Jan Vishwas Bill, 2022 has been referred to a 31-member joint parliamentary committee for scrutiny. The committee includes members from the Lok Sabha and the Rajya Sabha and will submit its report to parliament by the second part of the Budget session in 2023, The members from the Lok Sabha include PP Chaudhary, Sanjay Jaiswal, Queen Ojha, Rajendra Agrawal, Gaurav Gogoi, A Raja, Rajendra Agarwal, Poonam Pramod Mahajan, and Sougata Ray.
Introduction
Agentic AI systems are autonomous systems that can plan, make decisions, and take actions by interacting with external tools and environments. But they shift the nature of risk by blurring the lines among input, decision, and execution. A conventional model generates an output and stops. An agent takes input, makes plans, invokes tools, updates its state and repeats the cycle. This creates a system where decisions are continuously revised through interaction with external tools and environments, rather than being fixed at the point of input.
This means the attack surface expands in size and becomes more dynamic. Instead of remaining confined to components as in traditional computational systems, they spread in layers and can continue to grow through time. To understand this shift, the system can be analysed through functional layers such as inputs, memory, reasoning, and execution, while recognising that risk does not remain isolated within these layers but emerges through their interaction.
Agentic AI Attack Surface
A layered view of how risks emerge across input, memory, reasoning, execution, and system integration, including feedback loops and cross-system dependencies that amplify vulnerabilities.
Input Layer: Where Untrusted Data Becomes Control
The entry point of an agent is no longer one prompt. The documents, APIs, files, system logs and the outputs of other agents can now be considered input. This diversity is significant due to the fact that every source of input carries its own trust assumptions, and in the majority of cases, they are weak.
The most obvious threat is prompt injection, where inputs are treated as instructions rather than data. Since inputs are treated as instructions, a virus, a malicious webpage, or a document can contain instructions that override system goals without necessarily being detected as something harmful.
Indirect prompt injection extends this risk beyond direct user interaction. Instead of targeting the interface, attackers compromise the retrieval process by embedding malicious instructions within external data sources. When the agent retrieves and processes the data, it treats the embedded content as legitimate input. As a result, the attack is executed through normal reasoning processes, allowing the system to act on untrusted data without recognising the manipulation.
Data poisoning also occurs at runtime. In contrast to classical poisoning (where training data is manipulated), runtime poisoning distorts the agent’s perception of its environment as it runs. This can change decisions without causing apparent failures.
Obfuscation introduces another indirect attacker vector. Encoded instructions or complicated forms may bypass human review but remain readable to the model. This creates asymmetry whereby the system knows more about the attack than those operating it. Once compromised at this layer, the agent implements compromised instructions which affect downstream operations.
Context and Memory: Persistence of Influence
Agentic systems depend on memory to operate efficiently. They often retain context across sessions and frequently store information between sessions.
This introduces a different type of risk: persistence. Through memory poisoning, attackers can insert false or adversarial information into sorted context, which then influences future decisions. Unlike prompt injection, which is often limited to a single interaction, this effect carries forward. Over time, the agent begins to operate on a distorted internal state, shaping decisions in ways that may not be immediately visible.
Another issue is cross-session leakage. Information in a particular context may be replayed in a different context when memory is being shared or there is insufficient memory separation. This is specifically dangerous in those systems that combine retrieval and long-term storage. The context management in itself becomes a weakness. Agents are required to make decisions on what to retain and what to discard. This is susceptible to attackers who can flood the context or manipulate what is still visible and indirectly affect reasoning.
The underlying problem is structural. Memory turns data into a state. Once state is corrupted, the system cannot easily distinguish valid knowledge from adversarial influence.
The issue is structural. Memory converts temporary data into a persistent state. Once this state is weakened, the system cannot reliably separate valid information from adversarial influence, making recovery significantly more difficult.
Reasoning and Planning: Manipulating Intent Without Breaking Logic
The reasoning layer is where agentic AI stands apart from traditional systems. The model no longer reacts to inputs alone. It actively breaks down objectives, analyses alternatives, and ranks actions.
At the reasoning stage, the nature of risk shifts. The concern is no longer limited to injecting instructions, but to influencing how decisions are made. One example is goal manipulation, where the agent subtly reinterprets its objective and produces outcomes that are technically correct but strategically harmful. Reasoning hijacking operates within intermediate steps, altering how constraints are evaluated or how trade-offs are prioritised. The system may remain internally consistent, which makes such deviations difficult to detect.
Tool selection becomes a critical control point. Agents decide which tools to use and when, so influencing these choices can redirect execution without directly accessing the tools themselves. Hallucinations also take on a different role here. In static systems, they remain errors. In agentic systems, they can trigger actions. A perceived need or incorrect judgement can translate into real-world consequences.
This layer introduces probabilistic failure. The system is not fully weakened, but it is nudged towards decisions that appear reasonable yet are incorrect. The risk lies in how those decisions are justified.
Tool and Execution: When Decisions Gain Reach
Once an agent begins interacting with tools, its behaviour extends beyond the model into external systems. APIs, databases, and services become part of the execution path.
One key risk is the use of unauthorised tools. When agents operate with broad permissions, any manipulation of the upstream can be converted into real-world actions. This makes access control a central security concern. Command injection also takes a different form here. The agent generates commands based on its reasoning, so if that reasoning is compromised, the resulting actions may still appear valid despite being harmful.
External tool outputs introduce another risk. If these systems return corrupted or misleading data, the agent may accept it without verification and incorporate it into its decisions. It is also becoming increasingly reliant on third-part tools and plugins adds to this exposure. If these components are compromised, they can affect behaviour without directly attacking the core system, creating a supply-side risk.
At this stage, the agent effectively operates as an insider. It holds legitimate credentials and interacts with systems in expected ways, making misuse harder to identify.
Application and Integration: System-Level Exposure
Agentic systems rarely operate in isolation. They are embedded in larger environments, interacting with identity systems, business logic, and operational workflows.
Access control becomes a major vulnerability. Agents tend to operate across multiple systems with various permission models, creating irregularities that can be exploited. Risks also arise from identity and delegation. In case an agent is operating on behalf of a user, then any vulnerabilities in authentication or session management can allow attackers to assume that authority.
Workflow execution amplifies these risks. Agents can initiate multi-step processes such as transactions, updates, or approvals. Manipulating a single step can change the result of the entire workflow. As integrations increase, so do the number of interaction points, making cumulative risk harder to track.
At this layer, failures are not isolated. They propagate into business operations, making consequences harder to contain.
Output and Action: Where Failures Become Visible
The output layer is where failures become visible, though they rarely originate there.
Data leakage has been a key concern. Agents may disclose information they are allowed to access, especially when tasks boundaries are not clearly defined. Misinformation and unsafe outputs are also important, particularly when outputs directly influence actions or decisions.
Generated code and commands introduce execution risk. If outputs are used without validation, errors or manipulations can have system-level effects. The shift towards autonomous action increases this risk, as small upstream deviations can lead to significant consequences without human intervention. This layer reflects symptoms rather than root causes. Addressing it alone does not reduce the underlying risk.
Beyond Layers: The Missing Dimension
A layered view helps, but it does not capture the full picture. Agentic systems are defined by continuous interaction across layers.
The key missing dimension is the runtime loop. Inputs shape reasoning, reasoning drives action, and actions feed back into both reasoning and memory. These cycles create feedback loops, where small manipulations may escalate over time. This also reduces observability. With multiple interacting components, it becomes difficult to trace cause and effect or identify where failures originate.
Supply chain dependencies add another layer of risk. Models, datasets, APIs, and plugins each introduce their own points of failure. A compromise at any of these points can propagate across the system. The attack surface also includes governance. Weak supervision, unclear responsibility, or excessive autonomy increase overall risk. Human control is not external to the system; it is part of its security.
Conclusion: Structuring the Attack Surface
Agentic AI expands the attack surface beyond traditional systems. It is both recursive and stateful. Risk does not just accumulate across layers; it moves and changes as the system operates.
Any useful representation must go beyond a linear stack. It should capture feedback loops, persistent state, and cross-layer dependencies that characterise the way these systems actually behave. The system is not a pipeline but a cycle. That is where both its capability and its risk emerge.
A photo showing Cilia Flores, wife of Venezuelan President Nicolás Maduro, with visible injuries on her face is being widely shared on social media. Users claim the image was taken during her court appearance in the United States on January 5, alleging that she was beaten before being produced before a judge. Cyber Peace Foundation’s research found that the viral image was created using AI tools and is not real.
Claim:
A Facebook user shared the image claiming it shows Venezuelan President Maduro’s wife during her US court appearance, alleging physical assault prior to her arrest. The post also makes political and religious allegations in connection with the incident.Link, archive link and screenshot
Fact Check:
The viral image appeared suspicious due to unnatural facial details and injury patterns. Given the increasing use of artificial intelligence to generate fake visuals, Vishvas News analysed the image using AI image detection tools.TruthScan assessed the image as 93% likely to be AI-generated.
Sightengine flagged the image as 77% likely to be AI-generated.
The results indicate that the image is not authentic and has been created using AI tools.
What Official Reports Say
According to a CBS News report published on January 6, Nicolás Maduro and his wife Cilia Flores were produced before a federal court in Lower Manhattan, where they pleaded not guilty to drug trafficking and other charges. They are currently lodged at the Metropolitan Detention Center in Brooklyn The report states that the couple was detained during a US military operation. Following this, Venezuela’s Vice President Delcy Rodríguez was sworn in as the acting president. While Cilia Flores did appear before a Manhattan court, there is no authentic image showing her with injuries during the court proceedings. Link and Screenshot
https://www.cbsnews.com/live-updates/venezuela-trump-maduro-charges/
Conclusion:
The image being circulated as a photo of Cilia Flores during her US court appearance is AI-generated and fake. The claim that it shows injuries inflicted on her before being produced in court is false and misleading. The viral image has no connection with real court visuals.
