#FactCheck-No Evidence India Returned Iranian Oil After Trump-Modi Call

Introduction

In July 2025, the Digital Defence Report prepared by Microsoft raised an alarm that India is part of the top target countries in AI-powered nation-state cyberattacks with malicious agents automating phishing, creating convincing deepfakes, and influencing opinion with the help of generative AI (Microsoft Digital Defence Report, 2025). Most of the attention in the world has continued to be on the United States and Europe, but Asia-Pacific and especially India have become a major target in terms of AI-based cyber activities. This blog discusses the role of AI in espionage, redefining the threat environment of India, the reaction of the government, and what India can learn by looking at the example of cyber giants worldwide.

Understanding AI-Powered Cyber Espionage

Conventional cyber-espionage intends to hack systems, steal information or bring down networks. With the emergence of generative AI, these strategies have changed completely. It is now possible to automate reconnaissance, create fake voices and videos of authorities and create highly advanced phishing campaigns which can pass off as genuine even to a trained expert. According to the report made by Microsoft, AI is being used by state-sponsored groups to expand their activities and increase accuracy in victims (Microsoft Digital Defence Report, 2025). Based on SQ Magazine, almost 42 percent of state-based cyber campaigns in 2025 had AIs like adaptive malware or intelligent vulnerability scanners (SQ Magazine, 2025).

AI is altering the power dynamic of cyberspace. The tools previously needing significant technical expertise or substantial investments have become ubiquitous, and smaller countries can conduct sophisticated cyber operations as well as non-state actors. The outcome is the speeding up of the arms race with AI serving as the weapon and the armour.

India’s Exposure and Response 

The weakness of the threat landscape lies in the growing online infrastructure and geopolitical location. The attack surface has expanded the magnitude of hundreds of millions of citizens with the integration of platforms like DigiLocker and CoWIN. Financial institutions, government portals and defence networks are increasingly becoming targets of cyber attacks that are more sophisticated. Faking videos of prominent figures, phishing letters with the official templates, and manipulation of the social media are currently all being a part of disinformation campaigns (Microsoft Digital Defence Report, 2025).

According to the Data Security Council of India (DSCI), the India Cyber Threat Report 2025 reported that attacks using AI are growing exponentially, particularly in the shape of malicious behaviour and social engineering (DSCI, 2025). The nodal cyber-response agency of India, CERT-In, has made several warnings regarding scams related to AI and AI-generated fake content that is aimed at stealing personal information or deceiving the population. Meanwhile, enforcement and red-teaming actions have been intensified, but the communication between central agencies and state police and the private platforms is not even. There is also an acute shortage of cybersecurity talents in India, as less than 20 percent of cyber defence jobs are occupied by qualified specialists (DSCI, 2025).

Government and Policy Evolution

The government response to AI-enabled threats is taking three forms, namely regulation, institutional enhancing, and capacity building. The Digital Personal Data Protection Act 2023 saw a major move in defining digital responsibility (Government of India, 2023). Nonetheless, threats that involve AI-specific issues like data poisoning, model manipulation, or automated disinformation remain grey areas. The following National Cybersecurity Strategy will attempt to remedy them by establishing AI-government guidelines and responsibility standards to major sectors.

At the institutional level, the efforts of such organisations as the National Critical Information Infrastructure Protection Centre (NCIIPC) and the Defence Cyber Agency are also being incorporated into their processes with the help of AI-based monitoring. There is also an emerging public-private initiative. As an example, the CyberPeace Foundation and national universities have signed a memorandum of understanding that currently facilitates the specialised training in AI-driven threat analysis and digital forensics (Times of India, August 2025). Even after these positive indications, India does not have any cohesive system of reporting cases of AI. The publication on arXiv in September 2025 underlines the importance of the fact that legal approaches to AI-failure reporting need to be developed by countries to approach AI-initiated failures in such fields as national security with accountability (arXiv, 2025).

Global Implications and Lessons for India

Major economies all over the world are increasing rapidly to integrate AI innovation with cybersecurity preparedness. The United States and United Kingdom are spending big on AI-enhanced military systems, performing machine learning in security operations hubs and organising AI-based “red team” exercises (Microsoft Digital Defence Report, 2025). Japan is testing cross-ministry threat-sharing platforms that utilise AI analytics and real-time decision-making (Microsoft Digital Defence Report, 2025).

Four lessons can be distinguished as far as India is concerned.

• To begin with, the cyber defence should shift to proactive intelligence in place of reactive investigation. It is not only possible to detect the adversary behaviour after the attacks, but to simulate them in advance using AI.
• Second, teamwork is essential. The issue of cybersecurity cannot be entrusted to government enforcement. The private sector that maintains the majority of the digital infrastructure in India must be actively involved in providing information and knowledge.
• Third, there is the issue of AI sovereignty. Building or hosting its own defensive AI tools in India will diminish dependence on foreign vendors, and minimise the possible vulnerabilities of the supply-chain.
• Lastly, the initial defence is digital literacy. The citizens should be trained on how to detect deepfakes, phishing, and other manipulated information. The importance of creating human awareness cannot be underestimated as much as technical defences (SQ Magazine, 2025).

Conclusion 

AI has altered the reasoning behind cyber warfare. There are quicker attacks, more difficult to trace and scalable as never before. In the case of India, it is no longer about developing better firewalls but rather the ability to develop anticipatory intelligence to counter AI-powered threats. This requires a national policy that incorporates technology, policy and education.

India can transform its vulnerability to strength with the sustained investment, ethical AI governance, and healthy cooperation between the government and the business sector. The following step in cybersecurity does not concern who possesses more firewalls than the other but aims to learn and adjust more quickly and successfully in a world where machines already belong to the battlefield (Microsoft Digital Defence Report, 2025).

References:

• Microsoft Digital Defense Report 2025
• India Cyber Threat Report 2025, DSCI 
• Lucknow based organisations to help strengthen cybercrime research training policy ecosystem 
• AI Cyber Attacks Statistics 2025: How Attacks, Deepfakes & Ransomware Have Escalated, SQ Magazine 
• Incorporating AI Incident Reporting into Telecommunications Law and Policy: Insights from India. ‍
• The Digital Personal Data Protection Act, 2023

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

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We were all stunned and taken aback when multiple photos of streets in the U.S. surfaced with heavily drugged individuals loosely sitting on the streets, victims of a systematically led drug operation that has recently become a target of the Trump-led “tariff” war, which he terms as a war on drug cartels. The drug is a synthetic opioid, fentanyl, which is highly powerful and addictive. The menace of this drug is found in a country that has Wall Street and the largest and most powerful economy globally. The serious implications of drug abuse are not about a certain economy; instead, it has huge costs to society in general. The estimated cost of substance misuse to society is more than $820 billion each year and is expected to continue rising. 

On June 26, the International Day against Drug Abuse and Illicit Trafficking is observed globally. However, this war is waged daily for millions of people, not on streets or borders, but in bloodstreams, behind locked doors, and inside broken homes. Drug abuse is no longer a health crisis; it is a developmental crisis. The United Nations Office on Drugs and Crime has launched a campaign against this organised crime that says, “Break the Cycle’ attributing to the fact that de-addiction is hard for individuals. 

The Evolving Drug Crisis: From Alleyways to Algorithms

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The menace of Drug abuse and illicit trafficking has also taken strides in advancement, and what was once considered a street-side vice has made its way online in a faceless, encrypted, and algorithmically optimised sense. The online drug cartels operate in the shadows and often hide in plain sight, taking advantage of the privacy designed to benefit individuals. With the help of darknet markets, cryptocurrency, and anonymised logistics, the drug trade has transformed into a transnational, tech-enabled industry on a global scale. In an operation led by the U.S. Department of Justice’s Joint Criminal Opiod and Darknet Enforcement (JCODE) and related to Operation RapTor, an LA apartment was only to find an organised business centre that operated as a hub of one of the most prolific methamphetamine and cocaine distributors in the market. Aaron Pinder, Unit Chief of the FBI Hi-Tech Organised Crime Unit, said in his interview, “The darknet vendors that we investigate, they truly operate on a global scale.” On January 11, 2025, during the Regional Conference on “Drug Trafficking and National Security,” it was acknowledged how cryptocurrency, the dark web, online marketplaces, and drones have made drug trafficking a faceless crime. Reportedly, there has been a seven-fold increase in the drugs seized from 2004-14 to 2014-24. 

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India’s Response: Bridging Borders, Policing Bytes

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India has been historically vulnerable due to its geostrategic placement between the Golden Crescent (Afghanistan-Iran-Pakistan) and Golden Triangle (Myanmar-Laos-Thailand), and confronts a fresh danger from “click-to-consume’ narcotics. Although India has always adopted a highly sensitised approach, it holds an optimistic future outlook for the youth. Last year, to commemorate the occasion of International Day against Drug Abuse and Illicit Trafficking, the Department of Social Justice & Empowerment organised a programme to engage individuals for the cause. The Indian authorities are often seen coming down heavily on the drug peddlers and cartels, and to aid the cause, the Home Minister Amit Shah inaugurated the new office complex of the NCB’s Bhopal zonal unit and extension of the MANAS-2 helpline to all 36 states and UTs. The primary objectives of this step are to evaluate the effectiveness of the Narcotics Coordination Mechanism (NCORD), assess the progress of states in fighting drug trafficking, and share real-time information from the National Narcotics Helpline ‘MANAS’ portal with the Anti-Narcotics Task Force (ANTF) of states and UTs. 

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The United Nation’s War on Narcotics: From Treaties to Technology

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The United Nations Office on Drugs and Crime (UNODC) is leading the international response. It offers vital data, early warning systems, and technical support to the states fighting the drug problem. The UNODC incorporates cooperation in cross-border intelligence, overseeing the darknet activities, encouraging the treatment and harm reduction, and using anti-money laundering mechanisms to stop financial flows. India has always pledged its support to the UN led activities, and as per reports dated 26th March, 2025, India chaired the prestigious UN-backed Commission on Narcotic Drugs (CND) meeting held in vienna, wherein India highlighted the importance of opioids for medical purposes as well as the nation’s notable advancements in the field. 

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Resolution on June 26: From Commemoration to Commitment

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Let June 26 be more than a date on the calendar- let it echo as a call to action, a day when awareness transforms into action, and resolve becomes resistance. On this day, CyberPeace resolves the following:

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• To treat addicts as victims rather than criminals and to pitch for reforms to provide access to reasonably priced, stigma-free rehabilitation.
• To integrate anti-drug awareness into digital literacy initiatives and school curricula in order to teach frequently and early.
• To demand responsibility and accountability from online marketplaces and delivery services that unwittingly aid traffickers
• To tackle the demand side through employment, mental health services, and social protection, particularly for at-risk youth.

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References

• https://www.gatewayfoundation.org/blog/cost-of-drug-addiction/#:~:text=The%20estimated%20cost%20for%20substance,Alcohol%3A%20%24249%20billion 
• https://www.unodc.org/unodc/en/drugs/index-new.html 
• https://www.fbi.gov/news/stories/global-operation-targets-darknet-drug-trafficking 
• https://www.thehindu.com/news/national/dark-web-crypto-drones-emerge-as-challenges-in-fight-against-drug-trafficking-amit-shah/article69088383.ece 
• https://www.pib.gov.in/PressReleaseIframePage.aspx?PRID=2028704 
• https://www.newindianexpress.com/nation/2025/Mar/26/in-a-first-india-chairs-un-forum-on-narcotics-pledges-to-improve-access-to-pain-relief-and-palliative-care 

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