#FactCheck - Viral Post of Gautam Adani’s Public Arrest Found to Be AI-Generated

Introduction

Empowering today’s youth with the right skills is more crucial than ever in a rapidly evolving digital world. Every year on July 15th, the United Nations marks World Youth Skills Day to emphasise the critical role of skills development in preparing young people for meaningful work and resilient futures. As AI transforms industries and societies, equipping young minds with digital and AI skills is key to fostering security, adaptability, and growth in the years ahead.

Why AI Upskilling is Crucial in Modern Cyber Defence

Security in the digital age remains a complex challenge, regardless of the presence of Artificial Intelligence (AI). It is one of the biggest modern ironies, and not only that, it is a paradox wrapped in code, where the cure and the curse are written in the same language. The very hand that protects the world from cyber threats can very well be used for the creation of that threat. This being said, the modern-day implementation of AI has to circumvent the threats posed by it or any other advanced technology. A solid grasp of AI and machine learning mechanisms is no longer optional; it is fundamental for modern cybersecurity. The traditional cybersecurity training programs employ static content, which can often become outdated and inadequate for the vulnerabilities. AI-powered solutions, such as intrusion detection systems and next-generation firewalls, use behavioural analysis instead of just matching signatures. AI models are susceptible, nevertheless, as malevolent actors can introduce hostile inputs or tainted data to trick computers into incorrect classification. Data poisoning is a major threat to AI defences, according to Cisco's evidence.

As threats surpass the current understanding of cybersecurity professionals, a need arises to upskill them in advanced AI technologies so that they can fortify the security of current systems. Two of the most important skills for professionals would be AI/ML Model Auditing and Data Science. Skilled data scientists can sift through vast logs, from pocket captures to user profiles, to detect anomalies, assess vulnerabilities, and anticipate attacks. A news report from Business Insider puts it correctly: ‘It takes a good-guy AI to fight a bad-guy AI.’ The technology of generative AI is quite new. As a result, it poses fresh security issues and faces security risks like data exfiltration and prompt injections. 

Another method that can prove effective is Natural Language Processing (NLP), which helps machines process this unstructured data, enabling automated spam detection, sentiment analysis, and threat context extraction. Security teams skilled in NLP can deploy systems that flag suspicious email patterns, detect malicious content in code reviews, and monitor internal networks for insider threats, all at speeds and scales humans cannot match. 

The AI skills, as aforementioned, are not only for courtesy’s sake; they have become essential in the current landscape. India is not far behind in this mission; it is committed, along with its western counterparts, to employ the emerging technologies in its larger goal of advancement. With quiet confidence, India takes pride in its remarkable capacity to nurture exceptional talent in science and technology, with Indian minds making significant contributions across global arenas.

AI Upskilling in India

As per a news report of March 2025, Jayant Chaudhary, Minister of State, Ministry of Skill Development & Entrepreneurship, highlighted that various schemes under the Skill India Programme (SIP) guarantee greater integration of emerging technologies, such as artificial intelligence (AI), cybersecurity, blockchain, and cloud computing, to meet industry demands. The SIP’s parliamentary brochure states that more than 6.15 million recipients have received training as of December 2024. Other schemes that facilitate educating and training professionals, such as Data Scientist, Business Intelligence Analyst, and Machine Learning Engineer are,

• Pradhan Mantri Kaushal Vikas Yojana 4.0 (PMKVY 4.0)
• Pradhan Mantri National Apprenticeship Promotion Scheme (PM-NAPS)
• Jan Shikshan Sansthan (JSS)

Another report showcases how Indian companies, or companies with their offices in India such as Ernst & Young (EY), are recognising the potential of the Indian workforce and yet their deficiencies in emerging technologies and leading the way by internal upskilling and establishing an AI Academy, a new program designed to assist businesses in providing their employees with essential AI capabilities, in response to the increasing need for AI expertise. Using more than 200 real-world AI use cases, the program offers interactive, organised learning opportunities that cover everything from basic ideas to sophisticated generative AI capabilities. 

In order to better understand the need for these initiatives, a reference is significant to a report backed by Google.org and the Asian Development Bank; India appears to be at a turning point in the global use of AI. As per the research, “AI for All: Building an AI-Ready Workforce in Asia-Pacific,” India urgently needs to provide accessible and efficient AI upskilling despite having the largest workforce in the world. According to the paper, by 2030, AI could boost the Asia-Pacific region’s GDP by up to USD 3 trillion. The key to this potential is India, a country with the youngest and fastest-growing population.

Conclusion and CyberPeace Resolution

As the world stands at the crossroads of innovation and insecurity, India finds itself uniquely poised, with its vast young population and growing technologies. But to truly safeguard its digital future and harness the promise of AI, the country must think beyond flagship schemes. Imagine classrooms where students learn not just to code but to question algorithms, workplaces where AI training is as routine as onboarding. 

India’s journey towards digital resilience is not just about mastering technology but about cultivating curiosity, responsibility, and trust. CyberPeace is committed to this future and is resolute in this collective pursuit of an ethically secure digital world. CyberPeace resolves to be an active catalyst in AI upskilling across India. We commit to launching specialised training modules on AI, cybersecurity, and digital ethics tailored for students and professionals. It seeks to close the AI literacy gap and develop a workforce that is both morally aware and technologically proficient by working with educational institutions, skilling initiatives, and industry stakeholders. 

References

1. https://www.helpnetsecurity.com/2025/03/07/ai-gamified-simulations-cybersecurity/ 
2. https://www.businessinsider.com/artificial-intelligence-cybersecurity-large-language-model-threats-solutions-2025-5?utm
3. https://apacnewsnetwork.com/2025/03/ai-5g-skills-boost-skill-india-targets-industry-demands-over-6-15-million-beneficiaries-trained-till-2024/ ‍
4. https://indianexpress.com/article/technology/artificial-intelligence/india-must-upskill-fast-to-keep-up-with-ai-jobs-says-new-report-10107821/

Executive Summary:

A photo allegedly shows an Israeli Army dog attacking an elderly Palestinian woman has been circulating online on social media. However, the image is misleading as it was created using Artificial Intelligence (AI), as indicated by its graphical elements, watermark ("IN.VISUALART"), and basic anomalies. Although there are  certain reports regarding the real incident in several news channels, the viral image was not taken during the actual event. This emphasizes the need to verify photos and information shared on social media carefully.

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Claims:

A photo circulating in the media depicts an Israeli Army dog attacking an elderly Palestinian woman.

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Fact Check:

Upon receiving the posts, we closely analyzed the image and found certain discrepancies that are commonly seen in AI-generated images. We can clearly see the watermark “IN.VISUALART” and also the hand of the old lady looks odd.

We then checked in AI-Image detection tools named, True Media and contentatscale AI detector. Both found potential AI Manipulation in the image.

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Both tools found it to be AI Manipulated. We then keyword searched for relevant news regarding the viral photo. Though we found relevant news, we didn’t get any  credible source for the image.

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The photograph that was shared around the internet has no credible source. Hence the viral image is AI-generated and fake.

Conclusion:

The circulating photo of an Israeli Army dog attacking an elderly Palestinian woman is misleading. The incident did occur as per the several news channels, but the photo depicting the incident is AI-generated and not real.

• Claim: A photo being shared online shows an elderly Palestinian woman being attacked by an Israeli Army dog.
• Claimed on: X, Facebook, LinkedIn
• Fact Check: Fake & Misleading

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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