#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
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.
Executive Summary:
A viral message is circulating claiming the Reserve Bank of India (RBI) has banned the use of black ink for writing cheques. This information is incorrect. The RBI has not issued any such directive, and cheques written in black ink remain valid and acceptable.
Claim:
The Reserve Bank of India (RBI) has issued new guidelines prohibiting using black ink for writing cheques. As per the claimed directive, cheques must now be written exclusively in blue or green ink.
Fact Check:
Upon thorough verification, it has been confirmed that the claim regarding the Reserve Bank of India (RBI) issuing a directive banning the use of black ink for writing cheques is entirely false. No such notification, guideline, or instruction has been released by the RBI in this regard. Cheques written in black ink remain valid, and the public is advised to disregard such unverified messages and rely only on official communications for accurate information.
As stated by the Press Information Bureau (PIB), this claim is false The Reserve Bank of India has not prescribed specific ink colors to be used for writing cheques. There is a mention of the color of ink to be used in point number 8, which discusses the care customers should take while writing cheques.
Conclusion:
The claim that the Reserve Bank of India has banned the use of black ink for writing cheques is completely false. No such directive, rule, or guideline has been issued by the RBI. Cheques written in black ink are valid and acceptable. The RBI has not prescribed any specific ink color for writing cheques, and the public is advised to disregard unverified messages. While general precautions for filling out cheques are mentioned in RBI advisories, there is no restriction on the color of the ink. Always refer to official sources for accurate information.
- Claim: The new RBI ink guidelines are mandatory from a specified date.
- Claimed On: Social Media
- Fact Check: False and Misleading
Introduction
In April 2026, a class action suit in a federal court in California rejuvenated one of the most basic assertions in digital communication: that private messages are private. The suit claims that Meta Platforms, its subsidiary WhatsApp, and third-party contractors such as Accenture could have accessed user messages even though it had long promised end-to-end encryption.
This case is not merely about a single company or a single platform. It poses more profound questions regarding the definition, communication and regulation of privacy in an age when digital infrastructure is becoming more and more incomprehensible or unprovable to regular users.
What the Lawsuit Actually Says
The suit was filed by plaintiffs Brian Y. Shirazi and Nida Samson, who alleged that WhatsApp, Meta and contractors had intercepted and shared private messages with third parties without their consent. The complaint states that the federal investigators were notified by the whistleblowers that employees of Meta and external contractors had access to the content of WhatsApp messages that were expected to be encrypted and inaccessible.
This directly puts into question the main privacy promise of WhatsApp. The platform has been promoting itself as an end-to-end encrypted service in which not even WhatsApp can read your messages. The case asserts that this assertion was deceptive in its application and that no one ever gave any consent prior to their messages being intercepted, stored, or read.
The plaintiffs are proposing to represent a nationwide class of users of WhatsApp who sent or received messages between April 5, 2016, and the current time and subclasses in California and Pennsylvania. The claims involve breach of contract, California laws on privacy and data violations, false advertising and the Pennsylvania Wiretapping and Electronic Surveillance Act.
It should be mentioned that they are allegations. Similar assertions have been refuted by Metacomet in the past, with the company asserting that its encryption frameworks ensure that the company cannot access the messages. The case is in progress, and no facts have been found.
The Grey Area No One Talks About
In order to see the significance of this lawsuit outside the court, it is useful to consider the way modern messaging platforms actually work. In principle, end-to-end encryption means that only the sender and receiver can decipher a message. Even the service provider should not be able to access the content.
However, there is a grey space that is seldom publicly discussed: content moderation. User reports, metadata analysis or restricted message review processes are common methods used by platforms to identify harmful content, like fraud, child exploitation, or spam. The complaint indicates that such moderation procedures might have opened avenues to the content of messages to human reviewers or automated systems more than users were made to think.
This is not the first time that privacy and safety are at odds. Many jurisdictions have also advocated access to encrypted communications through legal means in the name of national security or criminal investigations. What this suit does is put that tension into even more stark relief by asking whether platforms are really open with users about these trade-offs.
The Consent Problem
The emphasis on consent is one of the most significant implications of this case. The plaintiffs claim that the users were never warned that their messages would be accessed by the employees or third parties and were never provided with any meaningful option on the same.
This is where the case turns into a data governance issue, rather than a legal one. Most data protection models consider the legality of data processing to be based on whether the users know how their data is being processed or not. When the accusations are found to be true, then the matter is not technical. It would be a contractual and ethical failure, a disjuncture between what platforms promise and what they do.
The implications are huge to the billions of users who use WhatsApp to communicate, both personally and professionally, and even politically.
What This Means Going Forward
An effective attack on the encryption assertions of WhatsApp might have actual implications for the rest of the digital ecosystem. Users might start doubting that any platform can be really considered to guarantee privacy. The regulators can advocate more stringent disclosure policies and compulsory independent audits of encryption systems. Social networks might have to re-architect their moderation frameworks to make sure that safety features do not silently compromise privacy guarantees that they claim.
Meanwhile, there is a real policy dilemma in this case that cannot be disregarded. Complete privacy may preclude the capacity to identify abuse or hateful material. The manner in which that balance is achieved and, more to the point, the manner in which it is made transparent to users is an issue that has yet to be addressed by policymakers, civil society and the tech industry.
Other technical experts have also questioned the plausibility of the claims in the lawsuit at scale, noting that it would be an extraordinary undertaking to systematically bypass end-to-end encryption. This further supports the argument of independent verification mechanisms. The problem is that users should not be forced to decide what they should believe in more: corporate guarantees or legal charges. There must be rules that can be enforced which are above the two.
Conclusion: Beyond One Lawsuit
The WhatsApp class action is eventually concerning a structural issue within the digital economy. Users are expected to have faith in systems that they cannot observe, on the assertions that they cannot test themselves.
This case is a warning, regardless of whether the allegations are proved or not. Privacy cannot be based on marketing language. It needs legally binding norms, actual transparency in the treatment of data, and external control that will provide users with something more to hang on than a tagline.
References
- https://www.bitdefender.com/en-us/blog/hotforsecurity/lawsuit-claims-meta-can-access-whatsapp-messages-despite-end-to-end-encryption-2
- https://blog.cryptographyengineering.com/2026/02/02/whatsapp-encryption-a-lawsuit-and-a-lot-of-noise/
- https://www.bloomberg.com/news/articles/2026-01-25/lawsuit-claims-meta-can-see-whatsapp-chats-in-breach-of-privacy
- https://www.classaction.org/blog/despite-privacy-promises-meta-third-parties-read-and-store-whatsapp-messages-class-action-lawsuit-alleges
