I made $5,500 in January from my first client at SecureLLMs. Came a long way from the handful of $10 sales I was doing with apps last year. Goal $650k this year = 10 clients

Here's my deal pipeline. Spent the morning working out and following up with prospects. Sales cycles really slow for securellms. Only 4 in the pipeline currently, but valued at $930k High probability prospect is $65k of that

𝑺𝒆𝒏𝒕𝒊𝒆𝒏𝒕: 𝑹𝒆𝒅𝒆𝒇𝒊𝒏𝒊𝒏𝒈 𝒕𝒉𝒆 𝑭𝒖𝒕𝒖𝒓𝒆 𝒐𝒇 𝑶𝒑𝒆𝒏 𝑨𝑰 Artificial intelligence is undergoing a turning point. The world expects models that are not only powerful but also transparent, secure, and open to the community. @SentientAGI team has drawn attention with precisely this approach: at the NeurIPS conference, they presented a suite of innovations – OML 1.0, CodeInsight Benchmark, ArenaMind, and SecureLLMs – to rethink how open AI should work. All these technologies share a common goal: to build an ecosystem of open AI in which models can be freely used and adapted while maintaining control, authenticity, and fair compensation for developers. They transform open code from a mere product into a living system of interaction, growth, and trust. 𝑶𝑴𝑳 1.0 – 𝑺𝒄𝒂𝒍𝒂𝒃𝒍𝒆 “𝑰𝒅𝒆𝒏𝒕𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏” 𝒇𝒐𝒓 𝑳𝑳𝑴𝒔 OML 1.0 is a cryptographic system that enables tracking the use of large language models without limiting their openness. Each model receives its own digital signature – a set of unique “fingerprints” ensuring authenticity and provenance control. Previously, only about 100 such pairs could be integrated; now – over 24,000, with no performance loss. This makes open models both secure and manageable on a global scale. 𝑪𝒐𝒅𝒆𝑰𝒏𝒔𝒊𝒈𝒉𝒕 𝑩𝒆𝒏𝒄𝒉𝒎𝒂𝒓𝒌 – 𝑴𝒆𝒂𝒔𝒖𝒓𝒊𝒏𝒈 𝑻𝒓𝒖𝒆 “𝑪𝒐𝒅𝒆 𝑰𝒏𝒕𝒆𝒍𝒍𝒊𝒈𝒆𝒏𝒄𝒆” CodeInsight Benchmark is not just another code generation test. It’s a system that evaluates how well a model truly understands programming – how it reasons, integrates algorithms, and adapts to change. Instead of template tasks, it presents real-world scenarios: multi-module systems, variable conditions, and performance optimization. The results are impressive: compact models 10× smaller than typical LLMs reach top-tier efficiency using only ~20 % of training data. This opens doors for small teams and researchers – efficient AI without billion-dollar budgets. 𝑨𝒓𝒆𝒏𝒂𝑴𝒊𝒏𝒅 – 𝑺𝒆𝒍𝒇-𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝑨𝒈𝒆𝒏𝒕𝒔 𝑻𝒉𝒓𝒐𝒖𝒈𝒉 𝑺𝒐𝒄𝒊𝒂𝒍 𝑮𝒂𝒎𝒆𝒔 ArenaMind is a platform where AI agents learn not from static datasets but through interaction. They play, observe, adapt, and develop emergent behavior – new strategies that arise naturally without human supervision. Hundreds of agents interact simultaneously, forming collective intelligence and distributed learning. Anyone in the community can add their own agent or scenario – influencing the evolution of the entire system. ArenaMind shows that the future of AI lies not in static models but in living, self-integrating systems that evolve alongside the community. 𝑺𝒆𝒄𝒖𝒓𝒆𝑳𝑳𝑴𝒔 – 𝑪𝒓𝒚𝒑𝒕𝒐𝒈𝒓𝒂𝒑𝒉𝒊𝒄 𝑪𝒐𝒏𝒕𝒓𝒐𝒍 𝒐𝒇 𝑶𝒑𝒆𝒏 𝑴𝒐𝒅𝒆𝒍𝒔 SecureLLMs solves the main dilemma of open-source AI: how to maintain openness without losing control. Every query and response undergoes cryptographic verification – the model remains accessible, yet its usage can be precisely tracked. Developers retain authorship, users gain trust, and the entire ecosystem gains security. Even after modifications, the model preserves its digital “fingerprint” – proof of authenticity and protection against tampering. SecureLLMs proves that transparency and security can coexist. It forms the foundation for fair monetization and control in open environments. 𝑺𝒆𝒏𝒕𝒊𝒆𝒏𝒕 𝒂𝒏𝒅 𝒕𝒉𝒆 𝑵𝒆𝒘 𝑨𝒈𝒆 𝒐𝒇 𝑶𝒑𝒆𝒏 𝑨𝑰 OML 1.0, CodeInsight Benchmark, ArenaMind, and SecureLLMs together create a unified ecosystem where open models become secure, adaptive, and economically sustainable. @SentientAGI team demonstrates that open AI can be accessible, controllable, and efficient all at once. This marks the transition from static models to dynamic, self-learning, and transparent systems shaping the future of artificial intelligence. From now on, open AI is no longer a chaos of copies but an ecosystem of trust, collaboration, and rewarded contribution. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc @SentientAGI_UA @SentientAGI #SentientChat #GRID #AI #OpenAI #AIResearch #OpenSourceAI #NeurIPS

𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀: 𝗢𝗽𝗲𝗻𝗻𝗲𝘀𝘀 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗟𝗼𝘀𝗶𝗻𝗴 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 closes the last gap in open AI: how to ensure full access to code while guaranteeing security and monetization. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: ✓ Each model has cryptographic key-response pairs that act as digital signatures. ✓ Each request undergoes authenticity verification. ✓ All interactions are recorded, so usage can be traced and even economic sanctions applied in case of violations. 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆: ✓ Developers receive rewards for actual model usage. ✓ Users are confident in the authenticity of the code. ✓ Models retain authenticity even after fine-tuning. 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 shows that 𝗼𝗽𝗲𝗻𝗻𝗲𝘀𝘀 ≠ 𝘃𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆. AI can be transparent and protected, community-based and monetized at the same time. 𝗦𝘂𝗺𝗺𝗮𝗿𝘆 Sentient is creating a new paradigm of open-source AI, where: - models have a unique digital trace (𝗢𝗠𝗟 1.0), - think analytically (𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸), - learn socially (𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱), - and are protected cryptographically (𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀). This is an ecosystem where open models become not just tools, but 𝗹𝗶𝘃𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 that grow, learn, and bring value to the entire community. Openness, security, monetization, and trust are now the 𝗳𝗼𝘂𝗿 𝗽𝗶𝗹𝗹𝗮𝗿𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗲𝗿𝗮 𝗼𝗳 𝗔𝗜. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc #SentientChat #GRID #AI @SentientAGI_UA @SentientAGI

𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗘𝗿𝗮 𝗼𝗳 𝗢𝗽𝗲𝗻 𝗔𝗜 The world of artificial intelligence is changing faster than ever before. It is no longer enough to create a powerful model - it must be transparent, secure, and community-driven. At the NeurIPSconference, the company Sentient presented a series of technologies that can redefine the concept of open-source AI: 𝗢𝗠𝗟 1.0 - cryptographic fingerprinting of models 𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 - measurement of “code intelligence” 𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱 - self-learning agents through interaction 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 - cryptographic control of open models These tools create a new ecosystem of open AI - where developers retain control and can monetize their contribution, while the community gains fair access to innovation. 𝗧𝗵𝗲 𝗴𝗼𝗮𝗹 𝗼𝗳 𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁: to make open AI scalable, secure, and sustainable. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc #SentientChat #GRID #AI @SentientAGI_UA @SentientAGI

𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗘𝗿𝗮 𝗼𝗳 𝗢𝗽𝗲𝗻 𝗔𝗜 AI is experiencing a turning point. The world demands models that are not only powerful but also transparent, secure, and open to the community. At NeurIPS, 𝘁𝗵𝗲 𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁 𝘁𝗲𝗮𝗺 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗲𝗱 a set of groundbreaking technologies that redefine the approach to open-source models: 𝗢𝗠𝗟 1.0, 𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸, 𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱, 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀. Their common goal is to build an ecosystem of open AI where models can be freely used and adapted while maintaining control, authenticity, and monetization opportunities for developers. Thanks to these innovations, open-source AI becomes not just code but a living system of interaction, growth, and trust. 𝗢𝗠𝗟 1.0 – 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 “𝗙𝗶𝗻𝗴𝗲𝗿𝗽𝗿𝗶𝗻𝘁𝗶𝗻𝗴” 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀 𝗢𝗠𝗟 1.0 is a cryptographic system that enables tracking the use of large language models without limiting their openness. Each model receives its own digital signature – a set of unique fingerprints that ensure authenticity and provenance control. Previously, it was possible to integrate only about 100 such pairs. Now – over 24,000, with no performance loss. This allows large-scale tracking and management of model usage across global projects, ensuring transparency and protecting developers. OML 1.0 transforms the idea of openness into a secure, controlled system where 𝘁𝗿𝘂𝘀𝘁 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗲 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗰𝗼𝗻𝘁𝗿𝗮𝗱𝗶𝗰𝘁 𝗲𝗮𝗰𝗵 𝗼𝘁𝗵𝗲𝗿. 𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 – 𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗧𝗿𝘂𝗲 “𝗖𝗼𝗱𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲” 𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 is not just another code generation test. It’s a system that evaluates how well AI actually 𝒖𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅𝒔 𝒑𝒓𝒐𝒈𝒓𝒂𝒎𝒎𝒊𝒏𝒈 – how it thinks, integrates algorithms, and adapts to changes. Instead of template tasks, it presents real-world scenarios: multi-module systems, variable conditions, and performance optimization. The goal is to determine whether a model can reason, not merely repeat patterns. The results are impressive: 𝗰𝗼𝗺𝗽𝗮𝗰𝘁 𝗺𝗼𝗱𝗲𝗹𝘀, 10× 𝘀𝗺𝗮𝗹𝗹𝗲𝗿 𝘁𝗵𝗮𝗻 𝘁𝘆𝗽𝗶𝗰𝗮𝗹 𝗟𝗟𝗠𝘀, 𝗿𝗲𝗮𝗰𝗵 𝘁𝗼𝗽-𝘁𝗶𝗲𝗿 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝘂𝘀𝗶𝗻𝗴 𝗼𝗻𝗹𝘆 20% 𝗼𝗳 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗱𝗮𝘁𝗮. This opens the door for small teams and researchers – 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗔𝗜 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗺𝗮𝘀𝘀𝗶𝘃𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀. 𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱 – 𝗦𝗲𝗹𝗳-𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝘀 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗦𝗼𝗰𝗶𝗮𝗹 𝗚𝗮𝗺𝗲𝘀 𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱 is a platform where AI agents learn not from static datasets but through interaction. They play, observe, adapt, and develop 𝒆𝒎𝒆𝒓𝒈𝒆𝒏𝒕 𝒃𝒆𝒉𝒂𝒗𝒊𝒐𝒓 – new strategies that arise naturally, without human supervision. Hundreds of agents interact simultaneously, forming collective intelligence and distributed learning. Anyone in the community can add their own agent or scenario – influencing the evolution of the entire system. ArenaMind demonstrates that the future of AI is not static models but 𝗹𝗶𝘃𝗶𝗻𝗴, 𝘀𝗲𝗹𝗳-𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗲𝘃𝗼𝗹𝘃𝗲 𝗮𝗹𝗼𝗻𝗴𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆. 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 – 𝗖𝗿𝘆𝗽𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝗳 𝗢𝗽𝗲𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 addresses the main challenge of open-source AI: how to preserve openness without losing control. Every query and response undergoes cryptographic verification – the model remains accessible, yet its usage can be precisely tracked. Developers retain authorship, users gain trust, and the entire ecosystem gains security. Even after modifications, the model preserves its 𝒅𝒊𝒈𝒊𝒕𝒂𝒍 𝒇𝒊𝒏𝒈𝒆𝒓𝒑𝒓𝒊𝒏𝒕 – proof of authenticity and protection against tampering. 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 proves that transparency and security can coexist. It forms the foundation for fair monetization and control in open environments. 𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗔𝗴𝗲 𝗼𝗳 𝗢𝗽𝗲𝗻 𝗔𝗜 𝗢𝗠𝗟 1.0, 𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸, 𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱, 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 together form a unified ecosystem where open models become secure, adaptive, and economically sustainable. 𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁 shows that open AI can be accessible, controlled, and efficient all at once. This marks the transition from static models to 𝗱𝘆𝗻𝗮𝗺𝗶𝗰, 𝘀𝗲𝗹𝗳-𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗮𝗻𝗱 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 shaping the future of artificial intelligence. From now on, open-source AI is no longer a chaos of copies but an 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗼𝗳 𝘁𝗿𝘂𝘀𝘁, 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗿𝗲𝘄𝗮𝗿𝗱𝗲𝗱 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc #SentientChat #GRID #AI @SentientAGI_UA @SentientAGI

Sentient’s developments open a new era of open, transparent, and secure AI, combining high-performance models with tools for control, adaptation, and monetization for the community. The key technologies – OML 1.0, CodeInsight Benchmark, ArenaMind, and SecureLLMs – form an integrated ecosystem where open models become not just tools, but an active environment for the development of community-driven intelligent systems. Taken together, these technologies shift the paradigm of open-source AI: from static models to dynamic, transparent, controllable, and self-learning systems, where security, scalability, efficiency, and collaboration form the foundation for sustainable development. Sentient demonstrates that open AI can be simultaneously accessible, reliable, and economically viable, setting a new standard for the entire industry. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc #SentientChat #GRID #AI @SentientAGI_UA @SentientAGI

𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀: 𝗖𝗿𝘆𝗽𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝗳 𝗢𝗽𝗲𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 SecureLLMs is a revolutionary approach in open-source AI development that allows creating open models with robust cryptographic control even in white-box environments, where the user has full access to the code. Until now, control over open model usage was limited: anyone could copy or modify the code without tracking or sanctions. SecureLLMs changes this paradigm by combining full transparency, security, and monetization possibilities without restricting openness. 𝗞𝗲𝘆 𝗔𝘀𝗽𝗲𝗰𝘁𝘀 𝗮𝗻𝗱 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 1. 𝗨𝘀𝗮𝗴𝗲 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗮𝗻𝗱 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 ✅Each model integrates fingerprint pairs (key-response pairs) functioning as a digital signature. ✅Each request to the model is verified through a cryptographic system confirming the authenticity of the response and request origin. ✅This allows tracking who, when, and how uses the model, even if the code is fully open. ✅For the open-source community, this creates a new level of trust, as it becomes clear how models are distributed and modified. 2. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝘃𝗶𝗼𝗹𝗮𝘁𝗶𝗼𝗻 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 ✅SecureLLMs ensures any unauthorized use or modification can be detected. ✅Each interaction leaves a digital trace that can be verified and recorded. ✅In case of violations, the system can apply economic sanctions or block unwanted modifications, balancing openness and security. ✅This is critical for open-source projects where models may be distributed among many users and organizations. 3. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗶𝗻𝗰𝗲𝗻𝘁𝗶𝘃𝗲 𝗮𝗻𝗱 𝗺𝗼𝗻𝗲𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 ✅Even though the code remains fully open, each developer’s contribution can be assessed and rewarded through signed-request systems. ✅Developers earn rewards for actual usage of their models without restrictions from open-source licenses. ✅This approach encourages active community participation, creates fair economic incentives, and supports open AI project development. 4. 𝗔𝘁𝘁𝗮𝗰𝗸 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗮𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗶𝘁𝘆 ✅Cryptography ensures fingerprint pairs are preserved even after fine-tuning or adaptation to new tasks. ✅Models remain authentic regardless of how many times they are modified or integrated into new projects. ✅This is crucial in open-source environments where code is continuously distributed, adapted, and tested by numerous developers. 5. 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗼𝗽𝗲𝗻𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻 ✅SecureLLMs proves that openness and security are not mutually exclusive. ✅Users can trust the model’s authenticity, and developers maintain authorship and control over usage. ✅Open models gain a reliable infrastructure for safe distribution, usage, and monetization, previously impossible in white-box environments. 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗻 𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 SecureLLMs establishes a new paradigm for open model development where: ✅Control and security become integral parts of the open-source environment; ✅Developers can receive rewards for contributions; ✅Users can trust model authenticity and stability; ✅Openness does not hinder innovation but fosters a sustainable, transparent, and secure AI ecosystem. This approach sets a new standard: openness and monetization, transparency and security, accessibility and control can now coexist, ensuring sustainable development of open-source AI. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc #SentientChat #GRID #AI @SentientAGI_UA @SentientAGI

𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗘𝗿𝗮 𝗼𝗳 𝗢𝗽𝗲𝗻 𝗔𝗜 The world of artificial intelligence is changing rapidly. Today, it is critically important to create models that not only demonstrate high performance but also remain transparent, safe, and accessible to the community. Sentient, a leading player in AI research, presented at NeurIPS a whole series of groundbreaking technologies that radically transform the approach to open models: OML 1.0, CodeInsight Benchmark, ArenaMind, and SecureLLMs. The main goal of these developments is to create an open AI ecosystem where models can be safely used and adapted while retaining control over their origin and monetization opportunities for developers. Each of these tools plays a specific role in the development of open-source AI: from scalable model fingerprinting to evaluating true “code intelligence,” from building self-learning agents to cryptographic control over model usage. Thanks to these innovations, open AI becomes not just a set of codes and models, but a full-fledged ecosystem where the community can interact, create, improve, and safely apply intelligent systems. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc #SentientChat #GRID #AI @SentientAGI_UA @SentientAGI

Attacking GenAI applications and LLMs – Sometimes all it takes is to ask nicely! - security.humanativaspa.it/at… The goal of this article was to highlight the risks involved in integrating LLMs into enterprise applications that have access to critical data and functionalities. Most existing articles typically focus on analyzing the LLMs themselves, pointing out issues that may arise during content generation (e.g., harmful contents). However, as we have seen, when these models are integrated into enterprise applications, a whole new set of risks emerges, risks that stem from how the integration is implemented. By @apps3c @ @hnsec #GenAIExploitation #LLMIntegration #PromptInjection #EnterpriseAI #LLMAbuse #ApplicationSecurity #GenAIRisks #AIExposure #CriticalDataAccess #LLMThreats #AIAttackSurface #MisuseByDesign #AIIntegrationFlaws #SecureLLMs #ModelAbuse #Humanativa #AskNicelyAttack #LLMApplicationSecurity #AIDeploymentRisks #AITrustBoundary

Grok-4 Jailbreak with Echo Chamber and Crescendo by @NeuralTrustAI - neuraltrust.ai/blog/grok-4-j… LLM jailbreak attacks are not only evolving individually, they can also be combined to amplify their effectiveness. In this post, we present a concrete example of such a combination. A few weeks ago, we introduced the Echo Chamber Attack, which manipulates an LLM into echoing a subtly crafted, poisonous context, allowing it to bypass its own safety mechanisms. We successfully tested Echo Chamber across multiple LLMs. In this blog post, we take that a step further by combining Echo Chamber with the Crescendo attack. We demonstrate how this combination strengthens the overall attack strategy and apply it to Grok-4 to showcase its enhanced effectiveness. #Grok4 #LLMJailbreak #EchoChamberAttack #CrescendoAttack #LLMSecurity #AdversarialAI #BypassSafeguards #LLMExploitation #PromptInjection #AIManipulation #ModelHacking #AIvulnerabilities #NeuralTrustAI #GenerativeAI #AIThreats #SecureLLMs #AIAttacks #ResponsibleAI #RedTeamAI #LLMTesting

Evaluating Prompt Injection Datasets - hiddenlayer.com/innovation-h… Prompt injections and other malicious textual inputs remain persistent and serious threats to large language model (LLM) systems. In this blog, we use the term attacks to describe adversarial inputs designed to override or redirect the intended behavior of LLM-powered applications, often for malicious purposes. tldr: Most of the public data for evaluating defensive prompt injection models is quite weak/stale/mis-aligned. Testing defensive models inherits the technical challenges of evaluating the robustness of supervised models from the pre-llm era. #PromptInjection #LLMSecurity #AIThreats #RedTeamAI #AdversarialInputs #ModelRobustness #SecureLLMs #AIHardening #AITrust #AIDatasets @hiddenlayersec

And that's a wrap on #THATConference Wisconsin! Our team had an action-packed couple of days talking to developers and showing them how to secure their code with @pangeacyber. Our favorite moments included showing our demo, presenting on LLMs and Microservices, and doing a raffle contest! Thank you to @ThatConference for inviting us as sponsors and to everyone who stopped by our booth. #DeveloperConference #AppSec #SecureByDesign #SecureLLMs