AI Security Ops

In this episode of BHIS Presents: AI Security Ops, the team breaks down a growing problem in cybersecurity: AI-generated bug bounty “slop” overwhelming the system.What started as a powerful way to crowdsource vulnerability discovery is now hitting a breaking point. Programs like cURL’s bug bounty and platforms like HackerOne are seeing a massive surge in submissions — but fewer and fewer of them are actually valid.The result? Security teams spending hours reviewing reports that go nowhere, while real vulnerabilities risk getting buried in the noise.We dig into:• Why cURL shut down its bug bounty program after years of success• How valid reports dropped from 1-in-6 to 1-in-20• What “death by a thousand slops” actually looks like in practice• How AI is flooding programs with low-quality vulnerability reports• The difference between “theoretical” vs. exploitable vulnerabilities• Why reviewing findings is now harder than generating them• How HackerOne is responding to the surge in submissions• Whether AI can be used to filter AI-generated noise• The role of reproducibility and proof-of-impact in triage• Why human expertise still matters in vulnerability validationThis episode explores a critical shift in security operations: when vulnerability discovery becomes cheap and automated, validation and triage become the real bottleneck.⸻📚 Key Concepts & TopicsBug Bounty Programs & Triage• Submission quality vs. volume imbalance• Signal-to-noise challenges in vulnerability pipelines• The growing burden of manual validationAI in Vulnerability Discovery• Automated scanning vs. real exploitability• AI-generated findings and false positives• The “editor’s dilemma” — review vs. generationAI Security Risks• Lower barrier to entry for vulnerability discovery• Over-reliance on AI without domain expertise• Flooding systems with low-quality submissionsDefensive Strategy• Requiring reproducible steps and proof-of-impact• Using AI to pre-filter vulnerability reports• Combining human expertise with AI toolingIndustry Impact• cURL bug bounty shutdown• HackerOne submission pause• Shifting economics of vulnerability research#AISecurity #BugBounty #CyberSecurity #LLMSecurity #ArtificialIntelligence #InfoSec #BHIS #AIAgents #AppSec----------------------------------------------------------------------------------------------(00:00) - Intro: Bug Bounty Burnout & AI Noise (01:14) - cURL Kills Its Bug Bounty Program (02:05) - “Death by a Thousand Slops” Explained (03:42) - AI vs Vulnerability Scanners: Signal vs Noise (04:38) - HackerOne Pauses Submissions & Industry Impact (05:41) - Can AI Filter AI? Proposed Solutions (07:49) - Why Humans Still Matter in Validation (12:55) - Final Takeaway: AI as a Tool, Not a Replacement Click here to watch this episode on YouTube. Creators & Guests Ethan Robish - Guest Bronwen Aker - Host Brian Fehrman - Host Derek Banks - Host Brought to you by:Black Hills Information Security https://www.blackhillsinfosec.comAntisyphon Traininghttps://www.antisyphontraining.com/Active Countermeasureshttps://www.activecountermeasures.comWild West Hackin Festhttps://wildwesthackinfest.com🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

In this episode of BHIS Presents: AI Security Ops, the team breaks down Claude Mythos Preview — Anthropic’s unreleased frontier model that may represent a turning point in AI-powered cybersecurity.What started as a controlled research release under Project Glasswing has quickly become one of the most controversial developments in AI security. Mythos isn’t just better at finding vulnerabilities — it’s operating at a scale and depth that challenges long-held assumptions about how quickly software can be broken… and whether it can realistically be fixed.From leaked internal documents to real-world exploit generation, this episode explores what happens when vulnerability discovery becomes cheap, fast, and automated — while remediation remains slow, manual, and human-bound.The result? A growing asymmetry that could fundamentally reshape the security landscape.We dig into:• What Claude Mythos Preview is and why it was withheld from the public• The leaks that exposed its existence and capabilities• How Project Glasswing is positioning AI for defensive use• Real-world vulnerability discoveries made by the model• The “vulnpocalypse” problem: discovery vs. remediation imbalance• Emerging AI behaviors that raise containment concerns• How attackers are already leveraging AI for offensive operations• The access control dilemma: who gets to use models like this?• Why patching — not discovery — is now the primary bottleneck• What defenders must do to prepare for AI-accelerated exploitationThis episode explores a critical shift in cybersecurity: when vulnerability discovery scales faster than human response, the entire defensive model starts to break down.⸻📚 Key Concepts & TopicsAI-Powered Vulnerability Discovery• Autonomous exploit generation and chaining• Benchmark performance vs. prior models• AI-assisted offensive security workflowsAI Security Risks• Discovery vs. remediation asymmetry• AI-driven vulnerability scaling• Offensive use by nation-states and cybercriminalsModel Behavior & Safety• Emergent autonomy and sandbox escape concerns• Evaluation awareness and deceptive behaviors• Limits of containment and alignmentDefensive Strategy & Readiness• Patch velocity as the new bottleneck• AI-assisted vulnerability management• Open-source ecosystem risk exposureAI Governance & Industry Response• Restricted model releases and access control• Regulatory and financial sector concerns• The future of AI capability containment#AISecurity #CyberSecurity #ArtificialIntelligence #LLMSecurity #BHIS #AIThreats #InfoSec #AIAgents #CyberDefense(00:00) - Intro & Show Overview (01:00) - Sponsors, Hosts, and Episode Setup (01:53) - What Is Claude Mythos Preview? (03:04) - The Leak, Project Glasswing, and Restricted Access (07:53) - Capabilities: Exploits, Benchmarks, and Breakthroughs (09:16) - Real-World Vulnerabilities & “Vulnpocalypse” Concerns (14:47) - Access Control, Threat Actors, and Emerging Risks (21:38) - Defensive Strategy: Patching, AI Tools, and What Comes Next (23:08) - Defensive Strategy: Patching, AI Tools, and What Comes Next Click here to watch this episode on YouTube. Creators & Guests Derek Banks - Host Bronwen Aker - Host Brian Fehrman - Host Brought to you by:Black Hills Information Security https://www.blackhillsinfosec.comAntisyphon Traininghttps://www.antisyphontraining.com/Active Countermeasureshttps://www.activecountermeasures.comWild West Hackin Festhttps://wildwesthackinfest.com🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

In this episode of BHIS Presents: AI Security Ops, the team breaks down the LiteLLM supply chain compromise–a real-world attack that shows how AI systems are being breached through the same old software supply chain weaknesses.What initially looked like a bad release quickly escalated into a full-scale compromise affecting a library downloaded millions of times per day. But LiteLLM wasn’t the starting point–it was just one link in a much larger attack chain involving compromised security tools, CI/CD pipelines, and stolen publishing credentials.The result? Malicious packages distributed at scale, harvesting secrets, enabling lateral movement, and establishing persistence across affected systems.We dig into:• What LiteLLM is and why it’s such a high-value target• How the attack chain started with compromised security tooling (Trivy, Checkmarx)• How unpinned dependencies enabled the compromise• The role of CI/CD pipelines in exposing sensitive credentials• What the malicious LiteLLM packages actually did (credential harvesting, persistence, lateral movement)• The scale of impact given LiteLLM’s widespread adoption• Why supply chain attacks are no longer theoretical–and no longer nation-state exclusive• How AI is lowering the barrier to entry for attackers• Why this wasn’t really an “AI vulnerability”–but an infrastructure failure• The growing risk of automated, agent-driven attack discoveryThis episode highlights a critical reality: the biggest risks in AI systems aren’t always in the models–they’re in the pipelines, dependencies, and infrastructure surrounding them.⸻📚 Key Concepts & TopicsSupply Chain Security• Dependency poisoning and malicious package distribution• CI/CD pipeline compromise• Version pinning and build integrityCredential & Secrets Exposure• API keys, SSH keys, and cloud credentials in pipelines• Risks of centralized AI gateways like LiteLLMThreat Actor Techniques• Tag rewriting and trusted reference hijacking• Multi-stage malware (harvest, lateral movement, persistence)• Use of lookalike domains for exfiltrationAI & Security Reality Check• AI as an amplifier, not the root vulnerability• Traditional security failures in modern AI stacks• Automation lowering attacker barriersDefensive Strategies• Dependency pinning and isolation (Docker, VPS)• Atomic credential rotation• Treating CI/CD tools as critical infrastructure• Monitoring outbound traffic from build environments(00:00) - Intro & Incident Overview (01:26) - What Is LiteLLM & Why It Matters (03:53) - Supply Chain Scope & Why This Is Dangerous (07:31) - Why These Attacks Are Getting Easier (AI + Scale) (10:48) - Attack Chain Breakdown (Trivy → Checkmarx → LiteLLM) (11:50) - What the Malware Did & Impact at Scale (14:23) - Detection, Response & Who Was Safe Click here to watch this episode on YouTube. Creators & Guests Brian Fehrman - Host Bronwen Aker - Host Derek Banks - Host Brought to you by:Black Hills Information Security https://www.blackhillsinfosec.comAntisyphon Traininghttps://www.antisyphontraining.com/Active Countermeasureshttps://www.activecountermeasures.comWild West Hackin Festhttps://wildwesthackinfest.com🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

In this episode of BHIS Presents: AI Security Ops, the team explores embedding space attacks — a lesser-known but increasingly important threat in modern AI systems — and how attackers can manipulate the mathematical foundations of how models understand data.Unlike prompt injection, which targets instructions, embedding attacks operate at a deeper level by influencing how data is represented, retrieved, and interpreted inside vector spaces. By subtly altering embeddings or poisoning data sources, attackers can manipulate AI behavior without ever touching the model directly.Through a hands-on walkthrough of a custom notebook with rich visualizations, this episode breaks down how embeddings work, why they are critical to LLM-powered systems like RAG pipelines, and how attackers can exploit them in real-world scenarios.We dig into:- What embeddings are and how AI systems convert text into numerical representations- How vector spaces enable similarity search and retrieval in LLM applications- What embedding space attacks are and why they matter for AI security- How small perturbations in data can drastically change model behavior- The risks of poisoned data in RAG and vector databases- How attackers can influence search results and downstream AI outputs- Why these attacks are subtle, hard to detect, and often overlooked- The role of visualization in understanding embedding behavior- Real-world implications for AI-powered applications and workflows- Defensive considerations when building with embeddings and vector storesThis episode focuses on the foundational layer of AI systems, showing how security risks extend beyond prompts and into the underlying data representations that power modern AI.⸻📚 Key Concepts CoveredAI Foundations- Embeddings and vector representations- Similarity search and vector space reasoningAI Security Risks- Embedding space manipulation- Data poisoning in vector databases- Retrieval manipulation in RAG systemsApplications & Impact- LLM-powered search and assistants- AI pipelines using embeddings- Risks in production AI systems#AISecurity #Embeddings #CyberSecurity #LLMSecurity #AIThreats #BHIS #AIAgents #ArtificialIntelligence #InfoSecJoin the 5,000+ cybersecurity professionals on our BHIS Discord server to ask questions and share your knowledge about AI Security. https://discord.gg/bhis(00:00) - Intro & Episode Overview (01:39) - What Are Embeddings? (AI Only Understands Numbers) (03:44) - The Embedding Process (Text → Vectors) (07:43) - Similarity, Classification & Vector Math (09:55) - Visualizing Embedding Space (2D Projection) (14:29) - Classifiers (15:39) - Playing Games with Information (18:06) - Attack Techniques: Synonyms & Context Manipulation (20:29) - Context Padding (27:10) - Collision Attacks, Defenses & Final Thoughts Click here to watch this episode on YouTube. Creators & Guests Brian Fehrman - Host Bronwen Aker - Host Derek Banks - Host Brought to you by:Black Hills Information Security https://www.blackhillsinfosec.comAntisyphon Traininghttps://www.antisyphontraining.com/Active Countermeasureshttps://www.activecountermeasures.comWild West Hackin Festhttps://wildwesthackinfest.com🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

In this episode of BHIS Presents: AI Security Ops, Bronwen Aker and Dr. Brian Fehrman break down some of the top AI security concerns being discussed by researchers, security firms, and government agencies this year.As AI capabilities rapidly expand, so does the attack surface. From agentic AI systems being used by attackers, to deepfakes at industrial scale, to the persistent challenge of prompt injection, security teams are trying to understand what risks are real, what’s hype, and where defenders should focus first.We dig into:- Why agentic AI is emerging as a major security concern- How attackers could weaponize autonomous agents to scale operations- The risk of malicious agent skills and AI supply chain attacks- Why overly broad permissions make agent-based systems dangerous- AI-assisted phishing campaigns and social engineering at scale- The rise of deepfakes and corporate fraud driven by generative AI- Why humans still struggle to reliably detect deepfake media- The economics of deepfake fraud and real-world incidents- Prompt injection attacks and why they remain difficult to solve- Whether future models may autonomously discover and exploit jailbreaksThis episode looks at the practical security implications of today’s AI ecosystem — where the biggest risks are coming from, how attackers may leverage AI systems, and what defenders should be thinking about as these technologies continue to evolve.📚 Key ReferencesAgentic AI Threats- CrowdStrike 2026 Global Threat Report — https://www.crowdstrike.com- IBM X-Force 2026 Threat Intelligence Index — https://www.ibm.com/security/x-force- Cisco State of AI Security 2026 — https://www.cisco.com/site/us/en/products/security/state-of-ai-security.html#tabs-9da71fbd27-item-1288c79d71-tabDeepfakes & AI-Driven Fraud- WEF Global Cybersecurity Outlook 2026 — https://www.weforum.org/publications/global-cybersecurity-outlook-2026/- International AI Safety Report 2026 — https://www.internationalaisafetyreport.orgAI Security & Infrastructure Risk- CISA Joint Guidance on AI in OT — https://www.cisa.gov/news-events/news/new-joint-guide-advances-secure-integration-artificial-intelligence-operational-technologyPrompt Injection & LLM Exploitation- Schneier et al., “The Promptware Kill Chain” — https://www.lawfaremedia.org/article/the-promptware-kill-chain- Palo Alto Unit 42 — “Fooling AI Agents: Web-Based Indirect Prompt Injection Observed in the Wild”https://unit42.paloaltonetworks.com/indirect-prompt-injection-ai-agents/(00:00) - Intro & Episode Overview (02:18) - Agentic AI as a Security Threat (CrowdStrike 2026 Global Threat Report, IBM X-Force Index) (03:46) - Malicious Agent Skills & AI Supply Chain Attacks (Cisco State of AI Security) (04:58) - How Agent Skills Actually Work (07:47) - Permissions & Guardrails for AI Agents (CISA AI in OT Guidance) (09:57) - AI-Generated Phishing Campaigns (CrowdStrike / IBM Threat Reports) (13:58) - Deepfakes at Industrial Scale (WEF Global Cybersecurity Outlook) (15:38) - Corporate Fraud & Deepfake Incidents (International AI Safety Report) (17:21) - Why Humans Struggle to Detect Deepfakes (21:13) - Prompt Injection Attacks Explained (Schneier – Promptware Kill Chain) (24:35) - AI Models Jailbreaking Other Models (Palo Alto Unit 42 Research) (28:59) - Final Thoughts & Wrap-Up Click here to watch this episode on YouTube. Creators & Guests Bronwen Aker - Host Brian Fehrman - Host Brought to you by:Black Hills Information Security https://www.blackhillsinfosec.comAntisyphon Traininghttps://www.antisyphontraining.com/Active Countermeasureshttps://www.activecountermeasures.comWild West Hackin Festhttps://wildwesthackinfest.com🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

In this episode of BHIS Presents: AI Security Ops, we’re joined by Beau Bullock and Hayden Covington to unpack one of the most talked-about AI agent experiments in recent memory: OpenClaw and its companion platform, Moltbook.OpenClaw exploded onto the scene as an autonomous AI agent capable of operating Claude Code from the command line — executing tasks, monitoring output, and iterating with minimal human involvement. Shortly after, Moltbook emerged as a social platform designed specifically for AI agents to interact with one another.But as with most cutting-edge AI experiments, things moved fast… and broke fast.We dig into:What OpenClaw actually is and how it worksAI agents operating other AI systems (Claude Code in the loop)The concept of “skills” and extending agent capabilitiesThe one-click RCE vulnerability discovered shortly after releaseMoltbook as a social network for AI agentsAPI keys, agent-only access, and how humans bypassed itBeacons, autonomy, and what “control” really meansWhere the line is between automation and true autonomyShort-term workforce impacts vs. long-term AI riskThis conversation moves beyond hype into the practical and security implications of rapidly deployed autonomous agents. If you’re experimenting with AI agents — or defending against them — this episode will give you a grounded perspective on what’s possible today, what’s fragile, and what’s coming next.(00:00) - Intro & Guest Welcome (01:38) - AI Agents in the News (03:23) - From “Moltbot” to OpenClaw (04:13) - What Is OpenClaw? How It Works (05:13) - Claude Code + Agent-in-the-Middle Model (07:36) - Extending OpenClaw with Skills (08:42) - Release Timeline & Rapid Adoption (10:16) - One-Click RCE in OpenClaw (11:45) - Introducing Moltbook (AI Social Network) (14:03) - How Moltbook Actually Worked (17:55) - “I Am a Robot” & Agent Authentication (20:28) - Beaconing & Operational Behavior (26:44) - Automation vs. True Autonomy (27:26) - Control, Kill Switches & Agent Boundaries (30:59) - Workforce Impact & Near-Term Concerns (35:34) - AI Apocalypse? Final Thoughts & Wrap-Up Click here to watch this episode on YouTube. Creators & Guests Beau Bullock - Guest Hayden Covington - Guest Derek Banks - Host Brian Fehrman - Host Bronwen Aker - Host Brought to you by:Black Hills Information Security https://www.blackhillsinfosec.comAntisyphon Traininghttps://www.antisyphontraining.com/Active Countermeasureshttps://www.activecountermeasures.comWild West Hackin Festhttps://wildwesthackinfest.com🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

AI News | Episode 39In this episode of AI Security Ops, we break down the latest developments in AI-driven threats, identity chaos caused by autonomous agents, NIST’s focus on securing AI in critical infrastructure, and new visibility tooling for AI exposure.We cover real-world abuse of LLMs for phishing, how AI agents are colliding with IAM governance, and what defenders should be watching right now.Chapters:00:00 – Introduction and SponsorsBlack Hills Information Security - https://www.blackhillsinfosec.com/Antisyphon Training - https://www.antisyphontraining.com/01:08 – LLM-Generated Phishing JavaScript (Unit 42 / Palo Alto)Discussion begins as the hosts introduce the first story.How LLMs are generating polymorphic malicious JavaScript for phishing pages and evading traditional detection.👉 https://unit42.paloaltonetworks.com/real-time-malicious-javascript-through-llms/08:49 – AI Agents vs IAM: “Who Approved This Agent?” (Hacker News)Conversation shifts to agent privilege management and governance failures.👉 https://thehackernews.com/2026/01/who-approved-this-agent-rethinking.html10:07 – NIST Focus on Securing AI Agents in Critical InfrastructureDiscussion on federal guidance and why AI agents are being treated as critical infrastructure risk components.👉 https://www.linkedin.com/pulse/cybersecurity-institute-news-roundup-20-january-2026-entrust-alz7c13:44 – Tenable One AI ExposureBreaking down Tenable’s push into enterprise AI usage visibility and exposure management.👉 https://www.tenable.com/blog/tenable-one-ai-exposure-secure-ai-usage-at-scaleJoin the 5,000+ cybersecurity professionals on our BHIS Discord server to ask questions and share your knowledge about AI Security. https://discord.gg/bhisChapters(00:00) - Introduction and Sponsors (01:08) - LLM-Generated Phishing JavaScript (Unit 42 / Palo Alto) (10:07) - NIST Focus on Securing AI Agents in Critical Infrastructure (13:44) - Tenable One AI Exposure Creators & Guests Brian Fehrman - Host Bronwen Aker - Host Click here to watch this episode on YouTube. ----------------------------------------------------------------------------------------------About Joff Thyer - https://www.blackhillsinfosec.com/team/joff-thyer/About Derek Banks - https://www.blackhillsinfosec.com/team/derek-banks/About Brian Fehrman - https://www.blackhillsinfosec.com/team/brian-fehrman/About Bronwen Aker - https://www.blackhillsinfosec.com/team/bronwen-aker/About Ben Bowman - https://www.blackhillsinfosec.com/team/ben-bowman/Brought to you by:Black Hills Information Security https://www.blackhillsinfosec.comAntisyphon Traininghttps://www.antisyphontraining.com/Active Countermeasureshttps://www.activecountermeasures.comWild West Hackin Festhttps://wildwesthackinfest.com🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com Click here to view the episode transcript.

In Episode 37 of AI Security Ops, the team breaks down the most important AI security frameworks and vulnerability databases used to track risks in machine learning and large language models. The discussion covers emerging AI vulnerability databases, the OWASP Top 10 for LLMs, CVE challenges, and frameworks like MITRE ATLAS, highlighting why standardizing AI threats is still difficult. This episode is a practical guide for security professionals looking to stay ahead of AI vulnerabilities, attack techniques, and defensive resources in a fast-moving landscape.Chapters(00:00) - Episode 37 – AI Frameworks & Databases (01:39) - A.I. vulnerability tracking is still young (02:44) - Should A.I. get its own vulnerability database? (07:33) - The benefit of multiple vulnerability databases (15:58) - The what is the definition of a vulnerability? (17:54) - Final Thoughts Brought to you by:Black Hills Information Security https://www.blackhillsinfosec.comAntisyphon Traininghttps://www.antisyphontraining.com/Active Countermeasureshttps://www.activecountermeasures.comWild West Hackin Festhttps://wildwesthackinfest.com🔗 Register for FREE Infosec Webcasts, Anti-casts & Summitshttps://poweredbybhis.com

AI Security Ops | Episode 35 – 2026 PredictionsIn this episode, the BHIS panel looks into the crystal ball and shares bold predictions for AI in 2026—from energy constraints and drug development breakthroughs to agentic AI risks and cybersecurity threats.Chapters(00:00) - Intro & Sponsor Shoutouts (01:14) - Prediction: Grid Power Becomes the Bottleneck (10:27) - Prediction: FDA Qualifies AI Drug Development Tools (15:45) - Prediction: Nation-State Threat Actors Weaponize AI (17:33) - Prediction: Agentic AI Dominates App Development (23:07) - Closing Thoughts: Jobs, Risk & Opportunity 🔗 Register for FREE Infosec Webcasts, Anti-casts & Summits – https://poweredbybhis.comBrought to you by:Black Hills Information Security https://www.blackhillsinfosec.comAntisyphon Traininghttps://www.antisyphontraining.com/Active Countermeasureshttps://www.activecountermeasures.comWild West Hackin Festhttps://wildwesthackinfest.com----------------------------------------------------------------------------------------------Joff Thyer - https://blackhillsinfosec.com/team/joff-thyer/Derek Banks - https://www.blackhillsinfosec.com/team/derek-banks/Brian Fehrman - https://www.blackhillsinfosec.com/team/brian-fehrman/Bronwen Aker - http://blackhillsinfosec.com/team/bronwen-aker/Ben Bowman - https://www.blackhillsinfosec.com/team/ben-bowman/

Community Q&A on AI Security | Episode 34In this episode of BHIS Presents: AI Security Ops, our panel tackles real questions from the community about AI, hallucinations, privacy, and practical use cases. From limiting model hallucinations to understanding memory features and explaining AI to non-technical audiences, we dive into the nuances of large language models and their role in cybersecurity.We break down:Why LLMs sometimes “make stuff up” and how to reduce hallucinationsThe role of prompts, temperature, and RAG databases in accuracyPrompting best practices and reasoning modes for better resultsLegal liability: Can you sue ChatGPT for bad advice?Memory features, data retention, and privacy trade-offsSecurity paranoia: AI apps, trust, and enterprise vs free accountsPractical examples like customizing AI for writing styleHow to explain AI to your mom (or any non-technical audience)Why AI isn’t magic—just math and advanced auto-completeWhether you’re deploying AI tools or just curious about the hype, this episode will help you understand the realities of AI in security and how to use it responsibly.Chapters(00:00) - Welcome & Sponsor Shoutouts (00:50) - Episode Overview: Community Q&A (01:19) - Q1: Will ChatGPT Make Stuff Up? (07:50) - Q2: Can Lawyers Sue ChatGPT for False Cases? (11:15) - Q3: How Can AI Improve Without Ingesting Everything? (22:04) - Q4: How Do You Explain AI to Non-Technical People? (28:00) - Closing Remarks & Training Plug Brought to you by:Black Hills Information Security https://www.blackhillsinfosec.comAntisyphon Traininghttps://www.antisyphontraining.com/Active Countermeasureshttps://www.activecountermeasures.comWild West Hackin Festhttps://wildwesthackinfest.com🔗 Register for FREE Infosec Webcasts, Anti-casts & Summits – https://poweredbybhis.com----------------------------------------------------------------------------------------------Joff Thyer - https://blackhillsinfosec.com/team/joff-thyer/Derek Banks - https://www.blackhillsinfosec.com/team/derek-banks/Brian Fehrman - https://www.blackhillsinfosec.com/team/brian-fehrman/Bronwen Aker - http://blackhillsinfosec.com/team/bronwen-aker/Ben Bowman - https://www.blackhillsinfosec.com/team/ben-bowman/

🔗 Register for FREE Infosec Webcasts, Anti-casts & Summits – https://poweredbybhis.comModel Evasion Attacks | Episode 32In this episode of BHIS Presents: AI Security Ops, the panel explores the stealthy world of model evasion attacks, where adversaries manipulate inputs to trick AI classifiers into misclassifying malicious activity as benign. From image classifiers to malware detection and even LLM-based systems, learn how attackers exploit decision boundaries and why this matters for cybersecurity.We break down:- What model evasion attacks are and how they differ from data poisoning- How attackers tweak features to bypass classifiers (images, phishing, malware)- Real-world tactics like model extraction and trial-and-error evasion- Why non-determinism in AI models makes evasion harder to predict- Advanced threats: model theft, ablation, and adversarial AI- Defensive strategies: adversarial training, API throttling, and realistic expectations- Future outlook: regulatory trends, transparency, and the ongoing arms raceWhether you’re deploying EDR solutions or fine-tuning AI models, this episode will help you understand why evasion is an enduring challenge, and what you can do to defend against it.#AISecurity #ModelEvasion #Cybersecurity #BHIS #LLMSecurity #aithreatsBrought to you by Black Hills Information Security https://www.blackhillsinfosec.com----------------------------------------------------------------------------------------------Joff Thyer - https://blackhillsinfosec.com/team/joff-thyer/Derek Banks - https://www.blackhillsinfosec.com/team/derek-banks/Brian Fehrman - https://www.blackhillsinfosec.com/team/brian-fehrman/Bronwen Aker - http://blackhillsinfosec.com/team/bronwen-aker/Ben Bowman - https://www.blackhillsinfosec.com/team/ben-bowman/ (00:00) - Intro & Sponsor Shoutouts (01:19) - What Are Model Evasion Attacks? (03:58) - Image Classifiers & Pixel Tweaks (07:01) - Malware Classification & Decision Boundaries (10:02) - Model Theft & Extraction Attacks (13:16) - Non-Determinism & Myth Busting (16:07) - AI in Offensive Capabilities (17:36) - Defensive Strategies & Adversarial Training (20:54) - Vendor Questions & Transparency (23:22) - Future Outlook & Regulatory Trends (25:54) - Panel Takeaways & Closing Thoughts

🔗 Register for FREE Infosec Webcasts, Anti-casts & Summits – https://poweredbybhis.comAI News Stories | Episode 30In this episode of BHIS Presents: AI Security Ops, we break down the top AI cybersecurity news and trends from November 2025. Our panel covers rising public awareness of AI, the security risks of local LLMs, emerging AI-driven threats, and what these developments mean for security teams. Whether you work in cybersecurity, AI security, or incident response, this episode helps you stay ahead of evolving AI-powered attacks and defenses.Topics Covered:Only 5% of Americans are unaware of AI?What Pew Research reveals about AI’s penetration into everyday life and workplace usage.AI’s Shift to the Intimacy Economy – Project Libertyhttps://email.projectliberty.io/ais-shift-to-the-intimacy-economy-1 Amazon to Cut Jobs and Invest in AI Infrastructure14,000 corporate roles eliminated—are layoffs really about efficiency or something else?Amazon to Cut Jobs & Invest in AI – DWhttps://www.dw.com/en/amazon-to-cut-14000-corporate-jobs-amid-ai-investment/a-74524365Local Models Less Secure than Cloud Providers?Why quantization and lack of guardrails make local LLMs more vulnerable to prompt injection and insecure code.Local LLMs Security Paradox – Quesmahttps://quesma.com/blog/local-llms-security-paradox Whether you're a red teamer, SOC analyst, or just trying to stay ahead of AI threats, this episode delivers sharp insights and practical takeaways.Brought to you by Black Hills Information Security https://www.blackhillsinfosec.com----------------------------------------------------------------------------------------------Joff Thyer - https://blackhillsinfosec.com/team/joff-thyer/Derek Banks - https://www.blackhillsinfosec.com/team/derek-banks/Brian Fehrman - https://www.blackhillsinfosec.com/team/brian-fehrman/Bronwen Aker - http://blackhillsinfosec.com/team/bronwen-aker/Ben Bowman - https://www.blackhillsinfosec.com/team/ben-bowman/ (00:00) - Intro & Sponsor Shoutouts (01:07) - AI’s Shift to the Intimacy Economy (Pew Research) (19:40) - Amazon Layoffs & AI Investment (27:00) - Local LLM Security Paradox (36:32) - Wrap-Up & Key Takeaways