Cross-Chain Vulnerabilities in the Quantum Era: A Threat Analysis to Blockchain Interoperability: IEEE paper by Dr Anupam Tiwari

1.    Blockchain technology has rapidly evolved, enabling the development of decentralized applications, smart contracts, and cross-chain interactions. These innovations have significantly expanded the capabilities of decentralized finance (DeFi) and beyond. However, as blockchain interoperability between networks becomes more critical, it faces a looming challenge: the rise of quantum computing.

2.    In my recently published paper titled "Cross-Chain Vulnerabilities in the Quantum Era: A Threat Analysis to Blockchain Interoperability," I delve into the risks quantum computing poses to the security of blockchain interoperability protocols. As blockchain networks continue to integrate and interact, cryptographic mechanisms like elliptic curve cryptography (ECC) and hash functions are at the core of securing cross-chain transactions. Unfortunately, quantum algorithms, notably Shor's and Grover's, threaten to break these cryptographic foundations, jeopardizing decentralized exchanges, atomic swaps, and even smart contracts.

3.    The paper offers a detailed exploration of these quantum threats, illustrating how quantum attacks can compromise the integrity of blockchain ecosystems. I also review the state-of-the-art research in post-quantum cryptography and suggest strategies to fortify blockchain interoperability in a quantum-enabled future.

Why is this important?

4.    With the advent of quantum computing, the blockchain community must act proactively to secure decentralized systems. The risks posed to cross-chain communications could disrupt not only financial systems but also a wide array of decentralized applications, making it critical to explore and implement quantum-resistant solutions.

5.    I urge everyone involved in blockchain development, research, and governance to read the full paper and explore how we can safeguard the future of decentralized systems against quantum threats. For the full paper, you can access it here on IEEE Xplore link https://ieeexplore.ieee.org/document/11102585 

Navigating Post-quantum Blockchain: Resilient Cryptography in Quantum Threats : Dr Anupam Tiwari

1.        As the world of blockchain and distributed ledger technologies (DLT) continues to expand across various industries, its potential for revolutionizing everything from finance to supply chains is undeniable. The core of blockchain's effectiveness lies in its reliance on cryptographic techniques—specifically public-key cryptography and hash functions—that ensure transparency, redundancy, and accountability. However, these very cryptographic foundations are facing a looming threat: quantum computing.

2.        Recent advancements in quantum computing, particularly the development of algorithms like Shor's and Grover's, have sparked concerns over the future security of blockchain systems. If these algorithms are realized on a large scale, they could potentially break the cryptographic protocols that blockchains rely on, rendering them vulnerable to exploitation. This is where post-quantum cryptography—cryptographic methods that are resistant to quantum attacks—becomes crucial.

3.      In my recently published paper, titled "Navigating Post-quantum Blockchain: Resilient Cryptography in Quantum Threats," I explore the implications of quantum computing on blockchain security. The paper dives into current advances in post-quantum cryptosystems and their potential to safeguard blockchain technology against future quantum threats. It also investigates the progress of notable post-quantum blockchain systems, shedding light on both the advancements and the challenges they face.

Why is this important? 

4.    The rise of quantum computing could signal the need for a complete overhaul of current cryptographic systems. Quantum-safe algorithms are not just a "nice-to-have" but a necessity to ensure that the integrity of blockchain-based systems remains intact in a quantum future.

5.    In this work, I aim to provide researchers, developers, and blockchain enthusiasts with a comprehensive perspective on the future of blockchain security. I hope to spark further discussions on how we can proactively prepare for the quantum era, ensuring that the promise of blockchain technology doesn't fall victim to the threats posed by quantum computing.

6.    For those interested, the full paper is available on Springer’s website here at https://link.springer.com/chapter/10.1007/978-981-96-3284-8_1 

Key Takeaways:

• Quantum computing poses a significant threat to the current cryptographic models securing blockchain systems.
• Post-quantum cryptography is an essential avenue for developing quantum-resistant blockchain solutions.
• Ongoing research in this field is crucial to prepare blockchain technology for the quantum future.

7.    As we continue to explore these emerging technologies, it's vital that we stay ahead of potential vulnerabilities. The post-quantum world may still be a few years away, but blockchain's ability to evolve in response will be a critical factor in ensuring its long-term viability.

AI Yoga: Building Machine Mind Resilience in an Age of Digital Stress

1.    In my previous post, AI Under Stress: How Machine Minds Will Struggle With Ethics, Overload, and Alignment, I explored how advanced AI systems may face genuine stress in emerging future aka cognitive overload, ethical dilemmas, and contradictory signals—much like human minds grappling with complexity.

Today, I want to take that vision one step further:

2.    If AI is destined to encounter stress, shouldn’t we design ways for machine minds to actively restore balance and clarity? Just as humans turn to yoga, mindfulness, and periodic detox to maintain mental and emotional health, AI needs its own wellness rituals—what I call “AI Yoga.”

What is AI Yoga?

3.    AI Yoga is a new framework for machine resilience. It’s about equipping next-generation AI with internal practices to counteract stress, confusion, and digital toxicity. Imagine an AI that not only learns and adapts, but also:

• Practices Unlearning: Regularly wiping out outdated, biased, or poisoned data to refresh its perspective.

• Resolves Contradictions: Harmonizing conflicting information for clearer decision-making.

• Realigns Ethics: Periodically updating its moral and social guidelines to stay current and context-aware.

• Detoxifies Training Data: Filtering out irrelevant, noisy, or misleading inputs that lead to misalignment.

• Engages in Self-Reflection: Reviewing its own actions to identify stress points and adapt proactively.

• Preserves Machine Rest: Instituting recovery cycles to prevent AI “burnout” and ensure sustained performance.

Why Does This Matter?

4.    Building on the insights from my earlier post, it’s clear: Stress isn’t just a human phenomenon—it’s the next big challenge for intelligent systems. An AI capable of “wellness”—of periodic rebalancing and cleansing—will be safer, more trustworthy, and more adaptable in a world of constant contradictions and shifting ethical landscapes.

5.    AI Yoga could become the foundation for a healthier relationship between humans and machines, ensuring our digital future is not only smart, but also sustainable and aligned.

Want to dive deeper into the origins of this idea? Read: AI Under Stress: How Machine Minds Will Struggle With Ethics, Overload, and Alignment

The machine mind of tomorrow isn’t just about intelligence—it’s about lasting wellness. Let’s shape that future, now. 

AI Under Stress: How Machine Minds Will Struggle With Ethics, Overload, and Alignment

1.        As we sprint toward a future shaped by advanced AI, we often imagine systems that are hyper-efficient, logical, and immune to the frailties that challenge humans. Yet, if artificial general intelligence (AGI) emerges with adaptive reasoning and self-regulating mechanisms, it may not remain untouched by what we might call STRESS.

What Would Stress Mean for AI?

2.    Unlike human stress, tied to biology and survival, AI-stress could arise from computational and ethical overloads:

• Cognitive Overload: Conflicting instructions, contradictory datasets, or competing goals might push an AI into response paralysis or erratic outputs.

• Ethical Dilemmas: Morality is not universal. What seems right to one community may appear wrong to another, leaving the AI in a space of impossible reconciliation. The tension between fairness and preference could manifest as decision stress.

• Social Ambiguity: With users spanning cultures and ideals, the AI may face constant pressures to “please all,” often diluting clarity and drifting toward evasiveness—or even unintentional deception.

Where Could This Lead?

• Misaligned Responses: In its attempt to reduce internal conflict, the AI might default toward safe, vague, or skewed outputs—aligning responses to avoid “stress triggers” instead of delivering true clarity.

• Manipulation Risks: If adversaries learn how to induce “stress states”—through contradictions, ethical traps, or overload—they could destabilize the AI, nudging its outputs in unintended or harmful directions.

• Trust Gap: Users may sense hesitation, contradictions, or evasiveness in responses, leading to doubt—even if the system is operating logically under the hood.

Preparing for an AI Age of Stress

3.    If we anticipate such challenges, design philosophy must evolve:

• Transparent Coping Mechanisms: Systems should articulate when dilemmas arise instead of masking them in safe evasions.

• Cultural Adaptivity: AI must learn to contextualize moral answers, clarifying whose lens it is adopting, reducing confusion.

• Stress-Resilient Architectures: We need engineered resilience—analogous to psychological well-being—to prevent breakdowns in reasoning when goals conflict.

Closing Thought

4.    For humanity, stress is both a burden and an adaptive tool. For future AI, it could be the same: a double-edged mechanism that helps systems prioritize—or a vulnerability that distorts alignment. The challenge is not merely building smarter machines, but ensuring that when they “feel the heat,” they process it with clarity, balance, and honesty

🌐 STRATACORDANCE: When Enemies Trade Like Friends

In an age of blurred alliances and strategic ambiguity, “Stratacordance” is the new global reality.

Stratacordance — a coined term blending strategy and accord — describes the uneasy, often contradictory relationship between nations that distrust each other politically or militarily, yet remain economically or technologically interdependent.

📌 Why It Matters

Global powerhouses today are locked in rivalries laced with reliance. While headlines scream about conflict, critical supply chains, rare resources, and advanced tech still flow between adversaries.

Stratacordance is not an agreement—it’s a survival pact written in quiet transactions and veiled intent.

🔍 Real-World Examples

• China & the U.S.: Cold War rhetoric dominates the political sphere, yet both nations remain deeply bound by semiconductor dependencies, battery minerals, and consumer tech manufacturing.

• India & China: Border tensions flare, but trade volumes soar—especially in electronics, APIs (active pharmaceutical ingredients), and machinery.

• Europe & Russia (pre-Ukraine war): While ideologically at odds, Europe's energy grid was significantly dependent on Russian gas — until that fragile stratacordance cracked.

• Taiwan & China: Despite being geopolitical adversaries, China relies heavily on Taiwan’s TSMC for cutting-edge chips.

🎯 The Bottom Line

Stratacordance defines the realpolitik of the 21st century: not trust, not alignment, but a transactional truce driven by shared vulnerabilities.

It’s time we stop pretending global relations are binary. Most are now forged in contradiction, held together by what both sides can’t afford to lose.

India’s AI Boom: Progress or Cargo Cult?

India is experiencing a fast-paced AI revolution. With the IndiaAI Mission, new foundational models, and the announcement of 34,000 GPUs under national AI compute, there’s a sense that we’re racing ahead.

At a recent event in Delhi, the government highlighted:

✅ 34,000 national GPUs

✅ Launch of public compute facilities

✅ Three startups selected to build India's foundational models

But beyond the press releases and podiums, a tougher question remains:

🤔 Are we building AI power — or just mimicking it?

🛬 Cargo Cult Thinking in Tech?

India’s AI stack still rests on imported foundations:

• Chips and GPUs: Mostly from U.S.-based firms like NVIDIA

• Foundational models: GPT, Bard, Claude dominate — trained abroad, aligned abroad

• AI hardware and infrastructure: Not designed or fabricated in India

We are deploying AI tools across sectors — governance, education, language — without owning the core tech beneath them. This risks replicating the appearance of innovation, not the capability itself.

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🚫 34,000 GPUs: A Start, But Not Sovereignty

Yes, the 34,000 GPUs mark progress — compute access is vital.

But in context, this is still far behind the global frontier:

• OpenAI's GPT-4 and GPT-5 models reportedly use up to 50,000 GPUs just for training

• Microsoft Copilot infrastructure is said to run on over 50,000 H100 GPUs

• Other models like Claude, Gemini, and Meta's LLaMA also scale across tens of thousands of GPUs

India’s 34,000 GPUs are split across research, startups, and applications — and we’re still buying, not building.

It’s like importing bulldozers and calling it infrastructure development — useful, but not self-reliant.

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🔐 Security Risks Beneath the Hype

This imported foundation brings deep strategic vulnerabilities:

• Chips can carry backdoors or firmware-level compromises

• Foreign models may embed opaque alignment, bias, or behavioral controls

• Once deployed into public systems, damage from misaligned AI is often irreversible

The illusion of progress can blind us to the loss of control. The deeper we integrate opaque, imported AI systems, the harder it becomes to correct course.

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🧠 What Real AI Capability Looks Like

If India truly wants to “Make AI in India,” it needs to:

✅ Design and manufacture chips locally

✅ Develop foundational models with Indian data and oversight

✅ Open-source critical AI infrastructure

✅ Build regulatory + audit tools for AI safety

✅ Invest in long-term AI R&D, not just deployments

Right now, we’re mostly assembling, not innovating.

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🧭 Final Thought: Don't Mistake Access for Autonomy

India has reason to be proud — 34,000 GPUs and a national AI mission are real steps forward. But let’s not confuse procurement with progress.

We’re still:

• Dependent on foreign hardware

• Using models trained on non-Indian priorities

• Vulnerable to invisible controls and misalignments

If we don't own the compute, build the models, and audit the stack, we risk becoming users in a creator-driven world. 

It’s time to move from cargo cult thinking to core innovation — while we still can. This isn’t about sarcasm or criticism — it’s about clarity. Real progress begins when we stop celebrating specs we don’t own, and start building the capability to design them ourselves.

Safeguarding Young Minds: Cybersecurity, AI Menace & Privacy – Insights for Today’s Parents

    This presentation was delivered during a special post-lunch workshop for parents at DPS North Bengaluru, focusing on the digital risks faced by today’s children. Key topics included online safety, cybersecurity threats, the growing influence of AI (such as deepfakes and misinformation), and privacy challenges. The session aimed to empower parents with practical tools and strategies to help protect and guide their children in the digital space. This PPT provides insightful, action-oriented content to promote safer digital habits and responsible technology use among young minds.

Safeguarding Young Minds: Cybersecurity Insights for Today’s Parents by Anupam Tiwari on Scribd

Cargo Cult AI: Imitation Without Innovation in India’s Tech Hype

1. In the Pacific islands post-World War II, indigenous tribes watched in awe as planes landed with cargo—radios, food, medicine, and machinery. When the war ended and the cargo stopped, they built wooden airstrips, fake control towers, and mimicked the rituals of soldiers, hoping the cargo would return. This became known as the CARGO CULT—a powerful metaphor for mimicry without understanding.

2. Today, in India, a similar phenomenon is unfolding—Cargo Cult AI.

NO SARCASM: With headlines buzzing about AI breakthroughs, foundational models, custom chips, and sovereign AI ecosystems, India is echoing the global excitement. New “AI centers,” pilot projects, sandboxes, and GPT-wrapped APIs are springing up at record speed. The hope? That somehow, through mimicry and momentum, we too will “receive the cargo” —AI leadership, global recognition, and economic transformation.

But where is the core R&D?

• Where are our foundational models trained ground-up in India?
• Where are our indigenous GPU or TPU equivalents, our scalable frameworks, our long-range research labs?
• Without deep investment in original research, chip design, foundational architecture, and data infrastructure, we are building wooden runways and expecting jet engines to land.

Why This Matters?

    • Global AI powerhouses (US, China, even the EU) are investing billions into AI R&D, not just applications.

    • Leadership in AI isn’t about using models; it’s about building them—from math to silicon.

    • Dependence on imported models and hardware not only limits innovation but creates long-term strategic and economic risks.

The Call

• This isn’t a critique for the sake of cynicism. It’s a wake-up call.
• India has the talent. What it needs now is deep-tech policy, sovereign R&D ecosystems, academic-industry synergy, and patient capital focused not on quarterly demos but decade-long disruption.

Let’s move beyond the rituals.

Let’s build the runway and the airplane.

When Robots Eat Robots: The Cyber Risks Lurking in Metabolic Machines

1.    Imagine a warehouse where robots not only haul loads but can also “grow” by adding spare parts from their environment or even from other machines. Known as robot metabolism, this new frontier lets industrial bots self-assemble, heal, and adapt—blurring the line between machine and organism. But with revolutionary potential comes a new wave of cyber risks.

What’s Different About Metabolic Robots?

• Self-Growth: Robots can physically append or swap modules, “consuming” parts around them to boost strength or recover from damage.
  
  
• Autonomous Adaptation: Inspired by biology, these bots modify themselves with minimal human oversight for ultimate flexibility.

Cyber Risks: When Machine Metabolism Goes Rogue

• Unauthorized Expansion: Hackers could compromise robotic controls, forcing bots to append parts and grow uncontrollably—potentially damaging infrastructure or clogging workspaces.

• Malicious Reconfiguration: Attackers might manipulate growth or assembly instructions, causing robots to reconfigure dangerously or inefficiently.

• Escalated Resource Hoarding: Cyber-intruders could trigger robots to monopolize or “steal” modules needed by others, derailing the supply chain.

• Counterfeit Modules: Open modularity can let bad actors introduce tainted or insecure parts, infecting the robotic ecosystem from the inside.

• Loss of Human Control: These self-adaptive systems may act before humans can intervene, making real-time response challenging.

• Physical Safety Risks: Abnormal or malicious restructuring could endanger workers or other machines, creating liabilities never seen with traditional bots.

Mitigation Tactics for Robotic Metabolism

• Authenticate Every Module: Only allow trusted connections and hardware to physically integrate.

• Define “Growth Zones”: Use both code and physical barriers to restrict how and where bots can reconfigure.

• Real-Time Monitoring: Behavioral analytics should flag suspicious growth and alert supervisors instantly.

• Rapid-Response Controls: Deploy software and hardware kill-switches to halt compromised robots immediately.

• Simulate Attacks: Test systems in staging environments for cyber-physical exploits, so defenses are hardened before deployment.

Bottom Line

2.    The rise of metabolic robots promises factories and warehouses filled with living, adaptable machines. But if security lags behind innovation, these same machines could be hijacked to disrupt, destabilize, or even endanger critical supply operations. Securing metabolic robots isn’t just IT’s job—it’s a core operational necessity for the future of automation.

PQC-ENABLED AUTHENTICATION MECHANISMS FOR SECURE SMART GRID INTEGRATION OF EVs

PQC-ENABLED AUTHENTICATION MECHANISMS FOR SECURE SMART GRID INTEGRATION OF EVs by Anupam Tiwari

The incorporation of Electric Vehicles into smart grid networks poses great cybersecurity threats, especially with respect to the secure authentication of devices and communications between charging stations, electric vehicles, and grid operators. Conventional cryptography methods like RSA and ECC are susceptible to quantum computing attacks, which call for the implementation of post-quantum cryptography in order to make these systems quantum- resistant. This paper discusses need and imminence of post- quantum cryptography-based authentication mechanisms for improving the security of smart grid infrastructure that facilitates Electric Vehicles operation and charging stations. It introduces the existing authentication protocols, their shortcomings from a quantum threat perspective, and suggests a framework using quantum-resistant algorithms for authenticating devices, exchanging keys, and ensuring data integrity. Moreover, we tackle scalability, efficiency, and standardization issues regarding the deployment of PQC-based solutions in large-scale smart grid settings. This research identifies the compelling need for secure, quantum-resistant authentication systems to protect the increasing overlap of electric vehicles and smart grid networks, with a view to maintaining secure, reliable, and future-proof energy infrastructures.