Clint Gibler’s Post

LLMs exploiting one-day vulnerabilities raises both excitement and skepticism. The study’s findings about GPT-4 autonomously succeeding in 87% of tests highlight its potential, but Chris Rohlf’s critique reminds us to question the methodology and context. From my experience the key takeaway isn’t just about what these models can do—it’s about understanding their limitations and the real-world impact. Publicly available exploits and small datasets may not paint the full picture, but they do underscore the growing need for better governance and monitoring of AI in offensive security. This debate is critical as we move forward: Are we ready to embrace LLMs as tools in vulnerability discovery while ensuring they are used responsibly? The intersection of AI and cybersecurity is evolving rapidly, and collaboration is crucial to stay ahead. #AISecurity #Cybersecurity #LLMSecurity #ResponsibleAI #AIAttackSurface #AIThreats #EthicalAI

📢 Upcoming Masterclass: LLM Top 10 Vulnerabilities 🔐 We’re excited to invite you to our upcoming Masterclass on LLM Top 10 Vulnerabilities, presented by Assaf Sasson, a cybersecurity expert with over a decade of experience in the field. 🔍 Masterclass Overview: Dive deep into the top vulnerabilities impacting large language models (LLMs), including: 1️⃣ Prompt Injection 2️⃣ Data Poisoning 3️⃣ Model Inversion 4️⃣ Model Extraction 5️⃣ Adversarial Examples 6️⃣ Training Data Leakage 7️⃣ Bias and Fairness Issues 8️⃣ Inadequate Input Validation 9️⃣ Model Misconfiguration 🔟 Unauthorized Model Access 📅 Date: 19th September 🕒 Time: 14.30 GMT 💻 RSVP Now: https://lnkd.in/dZZyGnea Whether you're a cybersecurity professional or simply passionate about AI, this masterclass is essential for understanding the risks and securing LLMs. #Cybersecurity #LLM #AI #Masterclass #Vulnerabilities #DataProtection #Securzy

MLSecOps Community Podcast S2Ep10 "Evaluating RAG and the Future of LLM Security: Insights with LlamaIndex" Watch or listen to the full episode here --> https://lnkd.in/d39JtN53 Hosts Neal Swaelens and Oleksandr Yaremchuk sit down with special guest Simon Suo, co-founder and CTO of LlamaIndex. The group discusses many topics, including: - Challenges and considerations of integrating LLMs into various applications, emphasizing the importance of contextualizing LLMs within specific environments. - The evolution of retrieval-augmented generation (RAG) techniques and the future trajectory of LLM-based applications, including the significance of balancing performance with cost and latency in leveraging LLM capabilities. - LLM security concerns and the critical need for robust input and output evaluation to mitigate potential risks. - The potential vulnerabilities associated with LLMs, including prompt injection attacks and data leakage, underscoring the importance of implementing strong access controls and data privacy measures. - Efforts within the community to address security challenges and foster a culture of education and awareness. Listen or read the transcript here to learn more about the ongoing innovation in large language model-based applications, while remaining vigilant about LLM security considerations ➡️ https://hubs.ly/Q02v63y60 Thanks again for joining and sharing your insights, Simon! Also, stop by and visit Protect AI at RSA and learn more about our MLSecOps community! #MLSecOps #aisecurity #MLSec #airisk #machinelearning #ai #llm h#genai #llamaindex #protectai #cybersecurity #rag

❓How do we evaluate the cybersecurity knowledge of LLMs? Introducing CyberMetric-80, CyberMetric-500, CyberMetric-2000, and CyberMetric-10000—comprehensive Q&A benchmark datasets designed for this purpose. 📚 Using LLM and RAG, we created these datasets from NIST standards, research papers, and more, validated by experts over 200+ hours. We tested 25 top LLM models and involved 30 human participants in CyberMetric-80. 🔍 Results: GPT-4o, GPT-4-turbo, Mixtral-8x7B-Instruct, Falcon-180B-Chat, and GEMINI-pro 1.0 excelled, outperforming human participants, though experienced experts still surpassed smaller models like Llama-3-8B. 📖 Read the full paper here: https://lnkd.in/eaJvbmSK #Cybersecurity #AI #LLM #CyberMetric #Innovation

Penetration testing LLMs is the new frontier of cybersecurity. Here's what you'll find inside: - Setting the stage: We'll explore how to prepare to get ready for the pentesting. - SecAi's testing methodology: A deep dive into executing the test on large language models. - Addressing the findings: Taking intelligent actions on the test results.

Is your GenAI as secure as you want it to be? Penetration testing LLMs is the new frontier of cybersecurity. Here's what you'll find inside: - Setting the stage: We'll explore how to prepare to get ready for the pentesting. - SecAi's testing methodology: A deep dive into executing the test on large language models. - Addressing the findings: Taking intelligent actions on the test results. Are you an information security professional or simply a leader curious about the future of AI? Then this blog, written by Sripathy Venkataramani, is for you! Share, discuss, and let's keep AI on the right track. Start reading here: https://lnkd.in/dEma99bN #PenetrationTesting #AISecurity #MachineLearning #EthicalAI #Cybersecurity #GenerativeAI #LLM #Reaktr #ReaktrAI #SecAi

#follow Sripathy Venkataramani reaktr.ai for #secure #AI #genAI #ChatGPT Everyone knows the genAI LLM today, OpenAI, ChatGPT is coming with V5… with this massive intelligence built in next comes is how do we govern data-IN to data-Out. No one want to GIGO — garbage 🗑️ IN - Garbage OUT. And at the same time PII, HIPPA, SOC2 , GDPR, CCPA and other regulatory data exposed in wrong hands 🙌. So here come the 🧬 security inbuilt DNA 🧬 in your LLM and genAI models to secure your data 📈 data 📉 data 📊…. Share, discuss, and let's keep AI on the right track. Start reading here: https://lnkd.in/dEma99bN #PenetrationTesting #AISecurity #MachineLearning #EthicalAI #Cybersecurity #GenerativeAI #LLM #Reaktr #ReaktrAI #SecAi Krishna Jadhav

🔍 What exactly defines a security vulnerability? 🎯 In cybersecurity, the concept of a vulnerability is more complex than it seems. While common examples like buffer overflows or SQL injections are often cited, their relevance depends heavily on the context. For instance, mitigations like ASLR can neutralize some vulnerabilities, and an SQL injection on a public database may not breach privacy rules. At #Sec4AI4Sec, we’re exploring how the definition of vulnerabilities impacts AI and assurance methodologies. This is especially critical for AI systems, where specifications are often derived from datasets rather than formal requirements. 🏛 Our ongoing work focuses on: Investigating how vulnerability definitions affect our ability to demonstrate the effectiveness of cybersecurity solutions. Understanding how AI tools can identify, address, and even prevent vulnerabilities in various contexts. ➡ Dive deeper into this intriguing topic: https://lnkd.in/djmt8y2D #AI #Cybersecurity #TechInnovation #VulnerabilityManagement #Research #FutureOfTech #Sec4AI4Sec

🚀 Leveling up in LLM Security! 🛡️ Excited to share that I’ve successfully completed the Checkmarx Codebashing course on OWASP LLM Vulnerabilities! 🎓🔐 As AI and Large Language Models (LLMs) become more integrated into applications, security risks evolve alongside them. This training deepened my understanding of key attack vectors, from prompt injection to data leakage, reinforcing the importance of securing AI-driven applications. Big thanks to Checkmarx for providing a great learning experience! Looking forward to applying these insights to build more resilient systems. #LLMSecurity #AI #CyberSecurity #Checkmarx #SecureCoding #OWASP #ContinuousLearning

"AGI has been achieved!" (spoiler: no, it hasn't - this isn't clickbait) ... That was the headline I read last week which dove me into a ream of research papers. As it turns out, no. AGI had not been achieved. A clickbait article was posted to summarize (badly) a somewhat newly published Massachusetts Institute of Technology paper discussing Test-Time Training which demonstrated the highest ever ARC scores to date. What is ARC? I'm glad you asked, but you may not be. It's a test. A test which measures a generative model's ability to 'think' for itself (high level description). However, the concept of Test-Time Training is real. Imagine injecting into the parameters of a model adjustments as its generating responses to fine-tune those outputs. Highly specific and accurate, and producing exceptional results. I recalled this term being used from a different paper though, and not one about model training. In fact, back in January I read (and wrote about) a set of tactics applied against models to infiltrate them. In it, the use of test-time training to create backdoors in models which were both nearly impossible to detect and remediate (section 3 - page 9) was discussed. So in other words, the Test-Time Training in this new paper is just a backdoor model attack being used for non-malicious purposes. What did we learn today? There is no good or evil. There are only techniques and how those techniques are being applied. MIT Paper: https://lnkd.in/e8RhBCkH Deception Paper: https://lnkd.in/euTVaVVh #AI #Research #MIT #Cambridge #TestTimeTraining #CyberSecurity #Cyber #Governance #ARC