Be Aware of Lottery Scams
Introduction
The Online Lottery Scam involves a scammer reaching out through email, phone or SMS to inform you that you have won a significant amount of money in a lottery, instructing you to contact an agent at a specific phone number or email address that actually belongs to the fraudster. Once the agent is reached out to, the recipient will need to cover processing charges in order to claim the lottery reward. Upfront Paying is required in order to receive your reward. However, actual rewards come at no cost. Additionally, such defective 'offers’ often contain phishing attacks, tricking users into clicking on malicious links.
Modus Operandi
The common lottery fraud starts with a message stating that the receiver has won a large lottery prize. These messages are frequently crafted to imitate official correspondence from reputable institutions, sweepstakes, or foreign administrations. The scammers request the receiver to give personal information like name, address, and banking details, or to make a payment for taxes, processing fees, or legal procedures. After the victim sends the money or discloses their personal details, the scammers may vanish or persist in requesting more payments for different reasons.
Tactics and Psychological Manipulation
These fraudulent techniques mostly rely on psychological manipulation to work. Fraudsters by persuading the victims create the fake sense of emergency that they must act quickly in order to get the lottery prize. Additionally, they prey on people's hopes for a better life by convincing them that this unanticipated gain has the power to change their destiny. Many people fall prey to the scam because they are driven by the desire to get wealthy and fail to recognize the warning indications. Additionally, fraudsters frequently use convincing language and fictitious documentation that appears authentic, hence users need to be extra cautious and recognise the early signs of such online fraudulent activities.
Festive Season and Uptick in Deceptive Online Scams
As the festive season begins, there is a surge in deceptive online scams that aim at targeting innocent internet users. A few examples of such scams include, free Navratri garba passes, quiz participation opportunities, coupons offering freebies, fake offers of cheap jewellery, counterfeit product sales, festival lotteries, fake lucky draws and charity appeals. Most of these scams are targeted to lure the victims for financial gain.
In 2023, CyberPeace released a research report on the Navratri festivities scam where we highlighted the ‘Tanishq iPhone 15 Gift’ scam which involved fraudsters posing as Tanishq, a well-known jewellery brand, and offering fake iPhone 15 as Navratri gifts. Victims were lured into clicking on malicious links. CyberPeace issued a detailed advisory within the report, highlighting that the public must exercise vigilance, scrutinise the legitimacy of such offers, and take precautionary measures to shield themselves from falling prey to such deceptive cyber schemes.
Preventive Measures for Lottery Scams
To avoid lottery scams ,users should avoid responding to messages or calls about fake lottery wins, verify the source of the lottery, maintain confidentiality by not sharing sensitive personal details, approach unexpected windfalls with scepticism, avoid upfront payment requests, and recognize manipulative tactics by scammers. Ignoring messages or calls about fake lottery wins is a smart move. Verifying the source and asking probing questions is also crucial. Users are also advisednot to click on such unsolicited links of lottery prizes received in emails or messages as such links can be phishing attempts. These best practices can help protect the victims against scammers who pressurise victims to act quickly that led them to fall prey to such scams.
Must-Know Tips to Prevent Lottery Scams
● It is advised to steer clear of any communication that offers lotteries or giveaways, as these are often perceived as too good to be true.
● It is advised to refrain from transferring money to individuals/entities who are unknown without verifying their identity and credibility.
● If you have already given the fraudsters your bank account details, it is crucial to alert your bank immediately.
● Report any such incidents on the National Cyber Crime Reporting Portal at cybercrime.gov.in or Cyber Crime Helpline Number 1930.
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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.
Introduction
Data protection has been a critical aspect of advocacy and governance all across the world. Data fuels our cyber-ecosystem and strengthens the era of emerging technologies. All industries and sectors are now dependent upon the data of the user. The governments across the world have been deliberating internally to address the issue and legality of Data protection and privacy. The Indian government has witnessed various draft bills and policies focusing on Data protection over the years, and the contemporary bill is the Digital Personal Data Protection Bill, 2023, which was tabled at the Lok Sabha (Lower House of Parliament) on 03 August for discussions and parliamentary assent.
What is DPDP, 2023?
The goal of the complete and comprehensive Digital Personal Data Protection Bill of 2023 is to establish a framework for the protection of personal data in India. The measure acknowledges the significance of protecting personal data and seeks to strike a balance between the necessity to process personal data for legitimate purposes and the right of individuals to do so. The bill establishes a number of crucial expressions and ideas associated with the protection of personal data, including “data fiduciary,” “data principal,” and “sensitive personal data.” It also emphasises the duties of data fiduciaries, including the need to establish suitable security measures to preserve personal data and the need to secure data principals’ consent before processing their personal information. The measure also creates the Data Protection Board of India, which would implement its requirements and guarantee data fiduciaries’ compliance. The board will have the authority to look into grievances, give directives, and impose sanctions for non-compliance.
Key Features of the Bill
The bill tabled at the parliament has the following key features:
- The 2023 bill imposes reasonable obligations on data fiduciaries and data processors to safeguard digital personal data.
- Under the 2023 bill, a new Data Protection Board is established, which will ensure compliance, remedies and penalties.
- Under the new bill, the Board has been entrusted with the power equivalent to a civil court, such as the power to take cognisance in response to personal data breaches, investigate complaints, imposing penalties. Additionally, the Board can issue directions to ensure compliance with the act.
- The 2023 bill also secures more rights of Individuals and establishes a balance between user protection and growing innovations.
- The bill creates a transparent and accountable data governance framework by giving more rights to individuals.
- There is an Incorporation of Business-friendly provisions by removing criminal penalties for non-compliance and facilitating international data transfers.
- The new 2023 bill balances out fundamental privacy rights and puts reasonable limitations on those rights.
- The new data protection board will carefully examine the instance of non-compliance by imposing penalties on non-compiler.
- The bill does not provide any express clarity in regards to compensation to be granted to the Data Principal in case of a Data Breach.
- Under 2023 Deemed consent is there in its new form as ‘Legitimate Users’ pertaining to the conditions in regard to Sovernity and Intergrity of India.
- There is an introduction of the negative list, which restricts cross-data transfer.
Additionally, the measure makes special provisions for the processing of children’s personal data and acknowledges the significance of protecting children’s privacy. Additionally, it highlights the rights of the data subjects, including their right to access their personal information, their right to have wrong information corrected, and their right to be forgotten.
Drive4CyberPeace
A campaign was undertaken by CyberPeace to gain a critical understanding of what people understand about Data privacy and protection in India. The 4-month long campaign led to a pan-India interaction with netizens from different areas and backgrounds. The thoughts and opinions of the netizens were understood and collated in the form of a whitepaper which was, in turn, presented to Parliamentarians and government officials. The whitepaper laid the foundation of the recommendations submitted to the Ministry of Electronics and Information Technology as part of the stakeholder consultation.
Conclusion
Overall, the Digital Personal Data Protection Bill of 2023 is an important step towards safeguarding Indian citizens’ privacy and personal data. It creates a regulatory agency to guarantee compliance and enforcement and offers a thorough framework for data protection. The law includes special measures for the protection of sensitive personal data and the personal data of children and acknowledges the significance of striking a balance between the right to privacy and the necessity of data processing.
Executive Summary
A purported video of Lieutenant General Devendra Sharma is being shared on social media, falsely portraying him as saying that “five aviators who participated in Operation Sindoor are missing from our training list. CyberPeace Research Wingresearch found that the viral video is completely fabricated and generated using artificial intelligence. The Ministry of Defence has also dismissed the clip as an AI-generated deepfake. The investigation found that Lieutenant General Devendra Sharma, Commander of the Army Training Command, never made any such statement. The viral clip has been manipulated by inserting fake audio into footage from his original speech to spread a misleading narrative.
Claim
A user on social media platform X shared the viral video with the caption:“Fifty-nine officers were listed at the Combat Army Aviation Training School, but five officers are missing. They were involved in Operation Sindoor as pilots of four Rafale jets and one Su-30 aircraft…”
Fact Check
To verify the claim, we extracted several keyframes from the viral video and conducted a reverse-image search. During the investigation, we found the original video on ANI’s official X account. In the authentic footage, Lt Gen Sharma is seen speaking at a routine event at a training institution. His remarks focused on regular training-related matters, and he made no reference to missing pilots, Operation Sindoor, or any alleged losses.
https://x.com/ANI/status/2061731994971881667
In the next stage of the investigation, we found a post by the Ministry of Defence, Government of India, which also identified the viral clip as a deepfake.
https://www.facebook.com/watch/?v=1697698548109000
The ministry clarified that the video had been manipulated using AI-generated audio and did not reflect any genuine statement made by Lt Gen Devendra Sharma.
Conclusion
The viral video is a fabricated AI-generated deepfake. Lieutenant General Devendra Sharma never made the statement attributed to him about five missing aviators linked to Operation Sindoor. The original video was altered by inserting fake audio into authentic footage. The manipulated clip is being circulated online to spread misinformation and create confusion regarding the Indian Armed Forces.
