#FactCheck - A misleading video falsely shows Former Prime Minister of India Pandit Jawaharlal Nehru admitting he had no role in India's independence

Introduction

Deepfakes are artificial intelligence (AI) technology that employs deep learning to generate realistic-looking but phoney films or images. Algorithms use large volumes of data to analyse and discover patterns in order to provide compelling and realistic results. Deepfakes use this technology to modify movies or photos to make them appear as if they involve events or persons that never happened or existed.The procedure begins with gathering large volumes of visual and auditory data about the target individual, which is usually obtained from publicly accessible sources such as social media or public appearances. This data is then utilised for training a deep-learning model to resemble the target of deep fakes.

Recent Cases of Deepfakes-‍

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In an unusual turn of events, a man from northern China became the victim of a sophisticated deep fake technology. This incident has heightened concerns about using artificial intelligence (AI) tools to aid financial crimes, putting authorities and the general public on high alert.
During a video conversation, a scammer successfully impersonated the victim’s close friend using AI-powered face-swapping technology. The scammer duped the unwary victim into transferring 4.3 million yuan (nearly Rs 5 crore). The fraud occurred in Baotou, China.

AI ‘deep fakes’ of innocent images fuel spike in sextortion scams
Artificial intelligence-generated “deepfakes” are fuelling sextortion frauds like a dry brush in a raging wildfire. According to the FBI, the number of nationally reported sextortion instances came to 322% between February 2022 and February 2023, with a notable spike since April due to AI-doctored photographs. And as per the FBI, innocent photographs or videos posted on social media or sent in communications can be distorted into sexually explicit, AI-generated visuals that are “true-to-life” and practically hard to distinguish. According to the FBI, predators often located in other countries use doctored AI photographs against juveniles to compel money from them or their families or to obtain actual sexually graphic images.

Deepfake Applications

• Lensa AI.
• Deepfakes Web.
• Reface.
• MyHeritage.
• DeepFaceLab.
• Deep Art.
• Face Swap Live.
• FaceApp.

Deepfake examples

There are numerous high-profile Deepfake examples available. Deepfake films include one released by actor Jordan Peele, who used actual footage of Barack Obama and his own imitation of Obama to convey a warning about Deepfake videos.
A video shows Facebook CEO Mark Zuckerberg discussing how Facebook ‘controls the future’ with stolen user data, most notably on Instagram. The original video is from a speech he delivered on Russian election meddling; only 21 seconds of that address were used to create the new version. However, the vocal impersonation fell short of Jordan Peele’s Obama and revealed the truth.

The dark side of AI-Generated Misinformation

• Misinformation generated by AI-generated the truth, making it difficult to distinguish fact from fiction.
• People can unmask AI content by looking for discrepancies and lacking the human touch.
• AI content detection technologies can detect and neutralise disinformation, preventing it from spreading.

Safeguards against Deepfakes-

Technology is not the only way to guard against Deepfake videos. Good fundamental security methods are incredibly effective for combating Deepfake.For example, incorporating automatic checks into any mechanism for disbursing payments might have prevented numerous Deepfake and related frauds. You might also:

• Regular backups safeguard your data from ransomware and allow you to restore damaged data.
• Using different, strong passwords for different accounts ensures that just because one network or service has been compromised, it does not imply that others have been compromised as well. You do not want someone to be able to access your other accounts if they get into your Facebook account.
• To secure your home network, laptop, and smartphone against cyber dangers, use a good security package such as Kaspersky Total Security. This bundle includes anti-virus software, a VPN to prevent compromised Wi-Fi connections, and webcam security.

What is the future of Deepfake –

Deepfake is constantly growing. Deepfake films were easy to spot two years ago because of the clumsy movement and the fact that the simulated figure never looked to blink. However, the most recent generation of bogus videos has evolved and adapted.
There are currently approximately 15,000 Deepfake videos available online. Some are just for fun, while others attempt to sway your opinion. But now that it only takes a day or two to make a new Deepfake, that number could rise rapidly.

Conclusion-

The distinction between authentic and fake content will undoubtedly become more challenging to identify as technology advances. As a result, experts feel it should not be up to individuals to discover deep fakes in the wild. “The responsibility should be on the developers, toolmakers, and tech companies to create invisible watermarks and signal what the source of that image is,” they stated. Several startups are also working on approaches for detecting deep fakes.

Introduction
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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.

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Agentic AI Attack Surface

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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.
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Input Layer: Where Untrusted Data Becomes Control
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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.
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Context and Memory: Persistence of Influence
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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.
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Reasoning and Planning: Manipulating Intent Without Breaking Logic
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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.

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‍Tool and Execution: When Decisions Gain Reach
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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.
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Application and Integration: System-Level Exposure
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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.
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Output and Action: Where Failures Become Visible
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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.
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Beyond Layers: The Missing Dimension
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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.
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Conclusion: Structuring the Attack Surface
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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.

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Introduction 

In the interconnected world of social networking and the digital landscape, social media users have faced some issues like hacking. Hence there is a necessity to protect your personal information and data from scammers or hackers. In case your email or social media account gets hacked, there are mechanisms or steps you can utilise to recover your email or social media account. It is important to protect your email or social media accounts in order to protect your personal information and data on your account. It is always advisable to keep strong passwords to protect your account and enable two-factor authentication as an extra layer of protection. Hackers or bad actors can take control of your account, they can even change the linked mail ID or Mobile numbers to take full access to your account.

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Recent Incident

Recently, a US man's Facebook account was deleted or disabled by Facebook. He has sued Facebook and initiated a legal battle. He has contended that there was no violation of any terms and policy of the platform, and his account was disabled. In the first instance, he approached the platform. However, the platform neglected his issue then he filed a suit, where the court ordered Facebook's parent company, Meta, to pay $50,000 compensation, citing ignorance of the tech company. 

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Social media account recovery using the ‘Help’ Section

If your Facebook account has been disabled, when you log in to your account, you will see a text saying that your account is disabled. If you think that your account is disabled by mistake, in such a scenario, you can make a request to Facebook to ‘review’ its decision using the help centre section of the platform. To recover your social media account, you can go to the “Help” section of the platform where you can fix a login problem and also report any suspicious activity you have faced in your account. 

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Best practices to stay protected

1. Strong password: Use strong and unique passwords for your email and all social media accounts.
2. Privacy settings: You can utilise the privacy settings of the social media platform, where you can set privacy as to who can see your posts and who can see your contact information, and you can also keep your social media account private. You might have noticed a few accounts on which the user's name is unusual and isn’t one which you recognise. The account has few or no friends, posts, or visible account activity.
3. Avoid adding unknown users or strangers to your social networking accounts: Unknown users might be scammers who can steal your personal information from your social media profiles, and such bad actors can misuse that information to hack into your social media account. 
4. Report spam accounts or posts: If you encounter any spam post, spam account or inappropriate content, you can report such profile or post to the platform using the reporting centre. The platform will review the report and if it goes against the community guidelines or policy of the platform. Hence, recognise and report spam, inappropriate, and abusive content.
5. Be cautious of phishing scams: As a user, we encounter phishing emails or links, and phishing attacks can take place on social media as well. Hence, it is important that do not open any suspicious emails or links. On social media, ‘Quiz posts’ or ‘advertisement links’ may also contain phishing links, hence, do not open or click on such unauthenticated or suspicious links.

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Conclusion

We all use social media for connecting with people, sharing thoughts, and lots of other activities. For marketing or business, we use social media pages. Social media offers a convenient way to connect with a larger community. We also share our personal information on the platform. It becomes important to protect your personal information, your email and all your social media accounts from hackers or bad actors. Follow the best practices to stay safe, such as using strong passwords, two-factor authentication, etc. Hence contributing to keeping your social media accounts safe and secure.

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References:

• https://www.gpb.org/news/2023/07/11/facebook-wrongfully-deleted-his-account-he-sued-the-company-and-won
• https://consumer.ftc.gov/articles/how-recover-your-hacked-email-or-social-media-account

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