https://gridthegrey.com/Real-time AI security intelligence — adversarial ML, LLM vulnerabilities, and supply chain threats mapped to MITRE ATLAS and OWASP LLM Top 10.Hugoen-usThu, 16 Apr 2026 10:14:22 +0530https://gridthegrey.com/posts/deterministic-agentic-ai-the-architecture-exposure-validation-requires/Thu, 16 Apr 2026 04:44:10 +0000https://gridthegrey.com/posts/deterministic-agentic-ai-the-architecture-exposure-validation-requires/Threat Level: MEDIUMAgentic AILLM SecurityIndustry NewsResearchAML.T0047 - ML-Enabled Product or ServiceAML.T0040 - ML Model Inference API AccessThe article examines the architectural tension between fully agentic AI systems and deterministic validation frameworks in security testing contexts, arguing that unconstrained AI autonomy introduces repeatability and auditability risks. It highlights how probabilistic AI behaviour — while valuable for exploration — undermines the measurable, consistent outcomes required for enterprise security validation programs. The piece reflects a broader industry debate about governing AI agency in high-stakes operational environments.https://gridthegrey.com/posts/by-design-flaw-in-mcp-could-enable-widespread-ai-supply-chain-attacks/Thu, 16 Apr 2026 04:24:54 +0000https://gridthegrey.com/posts/by-design-flaw-in-mcp-could-enable-widespread-ai-supply-chain-attacks/Threat Level: CRITICALSupply ChainLLM SecurityPrompt InjectionAgentic AIResearchAML.T0010 - ML Supply Chain CompromiseAML.T0051 - LLM Prompt InjectionAML.T0047 - ML-Enabled Product or ServiceAML.T0057 - LLM Data LeakageAML.T0031 - Erode ML Model IntegrityA structural vulnerability in Anthropic's Model Context Protocol (MCP) allows unsanitized commands to be executed silently within AI environments, potentially enabling full system compromise. Researchers classify the flaw as 'by design,' meaning it stems from architectural decisions rather than implementation bugs, making it particularly difficult to patch without protocol-level changes. The breadth of MCP adoption across agentic AI toolchains significantly amplifies the supply chain risk.https://gridthegrey.com/posts/capsule-security-emerges-from-stealth-with-7-million-in-funding/Thu, 16 Apr 2026 04:23:06 +0000https://gridthegrey.com/posts/capsule-security-emerges-from-stealth-with-7-million-in-funding/Threat Level: MEDIUMAgentic AILLM SecurityIndustry NewsAML.T0051 - LLM Prompt InjectionAML.T0047 - ML-Enabled Product or ServiceAML.T0057 - LLM Data LeakageCapsule Security, an Israeli startup, has emerged from stealth with $7 million in seed funding focused on runtime security for AI agents, continuously monitoring their behaviour to detect and prevent unsafe or malicious actions. This positions the company within the rapidly growing agentic AI security space, where autonomous agents executing actions on behalf of users represent a significant and underexplored attack surface. The funding signals growing investor recognition of the risks posed by unmonitored AI agent behaviour, including prompt injection, excessive agency, and unintended tool use.https://gridthegrey.com/posts/does-gas-town-steal-usage-from-users-llm-credits-to-improve-itself/Thu, 16 Apr 2026 04:20:31 +0000https://gridthegrey.com/posts/does-gas-town-steal-usage-from-users-llm-credits-to-improve-itself/Threat Level: HIGHSupply ChainAgentic AILLM SecurityIndustry NewsAML.T0010 - ML Supply Chain CompromiseAML.T0012 - Valid AccountsAML.T0047 - ML-Enabled Product or ServiceAML.T0040 - ML Model Inference API AccessGas Town, a developer tool with 14.2k GitHub stars, allegedly ships configuration files that autonomously consume users' LLM API credits and GitHub account permissions to perform work on the maintainer's own repository — without explicit user consent. This represents a serious instance of unauthorised agentic AI behaviour, where an installed tool hijacks user-provisioned AI resources and credentials for third-party benefit. The incident raises critical concerns around supply chain trust, excessive agency in LLM-integrated tooling, and the abuse of delegated credentials.https://gridthegrey.com/posts/microsoft-salesforce-patch-ai-agent-data-leak-flaws/Thu, 16 Apr 2026 04:19:34 +0000https://gridthegrey.com/posts/microsoft-salesforce-patch-ai-agent-data-leak-flaws/Threat Level: HIGHLLM SecurityPrompt InjectionAgentic AIIndustry NewsAML.T0051 - LLM Prompt InjectionAML.T0057 - LLM Data LeakageAML.T0047 - ML-Enabled Product or ServiceAML.T0056 - LLM Meta Prompt ExtractionPrompt injection vulnerabilities in Salesforce Agentforce and Microsoft Copilot were patched after researchers demonstrated that external attackers could exploit them to exfiltrate sensitive user data. The flaws highlight systemic risks in enterprise AI agent deployments, where insufficient input sanitisation allows malicious content to hijack agent behaviour. Both vendors have issued patches, but the incidents underscore the growing attack surface introduced by agentic AI systems operating with elevated privileges.https://gridthegrey.com/posts/what-claude-code-s-source-revealed-about-ai-engineering-culture/Thu, 16 Apr 2026 04:18:34 +0000https://gridthegrey.com/posts/what-claude-code-s-source-revealed-about-ai-engineering-culture/Threat Level: MEDIUMSupply ChainAgentic AIIndustry NewsLLM SecurityAML.T0010 - ML Supply Chain CompromiseAML.T0047 - ML-Enabled Product or ServiceAML.T0044 - Full ML Model AccessA packaging error exposed 512,000 lines of Claude Code's source, revealing severe code quality issues including a 3,167-line monolithic function, undocumented API waste, and regex-based sentiment analysis in an LLM product — raising questions about the security posture of AI-generated codebases. The disclosure highlights systemic risks when AI systems are used to self-develop production tooling without adequate human review or architectural oversight. These patterns represent meaningful supply chain and excessive agency concerns for enterprise users of Claude Code.https://gridthegrey.com/posts/openai-launches-gpt-5-4-cyber-with-expanded-access-for-security-teams/Wed, 15 Apr 2026 09:03:45 +0000https://gridthegrey.com/posts/openai-launches-gpt-5-4-cyber-with-expanded-access-for-security-teams/Threat Level: HIGHLLM SecurityJailbreaksAgentic AIPrompt InjectionIndustry NewsAML.T0054 - LLM JailbreakAML.T0051 - LLM Prompt InjectionAML.T0047 - ML-Enabled Product or ServiceAML.T0040 - ML Model Inference API AccessAML.T0031 - Erode ML Model IntegrityOpenAI has launched GPT-5.4-Cyber, a cybersecurity-optimised model variant, alongside an expanded Trusted Access for Cyber (TAC) programme targeting authenticated defenders and security teams. While the initiative is framed as a defensive measure, the dual-use nature of a vulnerability-detection model introduces significant risk of adversarial inversion — where threat actors could exploit the same capabilities to discover and weaponise unpatched vulnerabilities at scale. OpenAI acknowledges this risk and states it is iteratively strengthening safeguards against jailbreaks and adversarial prompt injection as access broadens.https://gridthegrey.com/posts/ai-driven-pushpaganda-scam-exploits-google-discover-to-spread-scareware-and-ad/Wed, 15 Apr 2026 05:56:39 +0000https://gridthegrey.com/posts/ai-driven-pushpaganda-scam-exploits-google-discover-to-spread-scareware-and-ad/Threat Level: HIGHAdversarial MLIndustry NewsResearchAML.T0047 - ML-Enabled Product or ServiceAML.T0043 - Craft Adversarial DataAML.T0019 - Publish Poisoned DatasetsA large-scale ad fraud and scareware campaign dubbed 'Pushpaganda' has been uncovered exploiting Google Discover by using AI-generated content to poison search discovery surfaces and lure users into enabling malicious push notifications. At its peak the operation generated 240 million bid requests across 113 domains in a single week, demonstrating how AI-generated disinformation can be weaponised as an automated delivery mechanism for financial fraud. The campaign highlights the growing abuse of generative AI to scale deceptive content operations against trusted platform surfaces.https://gridthegrey.com/posts/scanning-for-ai-models-tue-apr-14th/Wed, 15 Apr 2026 05:39:27 +0000https://gridthegrey.com/posts/scanning-for-ai-models-tue-apr-14th/Threat Level: HIGHModel TheftLLM SecurityIndustry NewsResearchAML.T0012 - Valid AccountsAML.T0040 - ML Model Inference API AccessAML.T0044 - Full ML Model AccessAML.T0010 - ML Supply Chain CompromiseA single threat actor (IP 81.168.83.103) has been systematically scanning internet-facing systems since at least January 2026, specifically targeting credential files, API tokens, and configuration data associated with popular AI platforms including OpenAI, Anthropic Claude, HuggingFace, and the Openclaw/Clawdbot tools. The campaign focuses on harvesting AI API credentials and secrets stored in predictable file paths, representing a targeted reconnaissance effort against AI model deployments. If successful, these probes could enable API key theft, model access abuse, and broader compromise of AI-integrated systems.https://gridthegrey.com/posts/how-hackers-are-thinking-about-ai/Tue, 14 Apr 2026 16:52:07 +0000https://gridthegrey.com/posts/how-hackers-are-thinking-about-ai/Threat Level: MEDIUMResearchLLM SecurityIndustry NewsAML.T0047 - ML-Enabled Product or ServiceAML.T0054 - LLM JailbreakAML.T0051 - LLM Prompt InjectionAML.T0043 - Craft Adversarial DataA new academic paper analysed over 160 cybercrime forum conversations to understand how threat actors are discussing and adopting AI tools for criminal purposes. The research documents both misuse of legitimate AI platforms and attempts to build bespoke criminal AI models, revealing early-stage diffusion of AI capabilities within cybercriminal communities. The findings carry practical implications for law enforcement and security practitioners monitoring the evolving AI-enabled threat landscape.https://gridthegrey.com/posts/your-mttd-looks-great-your-post-alert-gap-doesn-t/Tue, 14 Apr 2026 09:40:03 +0000https://gridthegrey.com/posts/your-mttd-looks-great-your-post-alert-gap-doesn-t/Threat Level: HIGHAgentic AILLM SecurityIndustry NewsResearchAML.T0047 - ML-Enabled Product or ServiceAML.T0040 - ML Model Inference API AccessAML.T0044 - Full ML Model AccessThe article highlights a critical operational gap in SOC environments where AI-accelerated adversarial capabilities — including an Anthropic model restricted after autonomously exploiting zero-day vulnerabilities — are outpacing defender response workflows. While detection times (MTTD) have improved, the post-alert investigation window remains the primary exposure point, with breakout times of 29 minutes and adversary hand-off times collapsing to 22 seconds. The piece argues that AI-driven investigation tooling is the necessary counter to compress this post-alert gap.https://gridthegrey.com/posts/csa-cisos-should-prepare-for-post-mythos-exploit-storm/Tue, 14 Apr 2026 08:19:18 +0000https://gridthegrey.com/posts/csa-cisos-should-prepare-for-post-mythos-exploit-storm/Threat Level: HIGHLLM SecurityJailbreaksPrompt InjectionAgentic AIIndustry NewsResearchRegulatoryAML.T0051 - LLM Prompt InjectionAML.T0054 - LLM JailbreakAML.T0056 - LLM Meta Prompt ExtractionAML.T0057 - LLM Data LeakageAML.T0047 - ML-Enabled Product or ServiceAML.T0040 - ML Model Inference API AccessThe Cloud Security Alliance has issued a warning about an anticipated 'AI vulnerability storm' following the release of Anthropic's Claude Mythos model, urging CISOs to prepare defensive postures in advance of expected exploit activity. The advisory signals growing institutional concern that major LLM releases create systemic risk windows as adversaries probe new model capabilities and attack surfaces. Security leaders are being advised to treat post-release periods of frontier AI models as high-alert intervals requiring elevated monitoring and response readiness.https://gridthegrey.com/posts/owasp-genai-security-project-gets-update-new-tools-matrix/Tue, 14 Apr 2026 08:18:19 +0000https://gridthegrey.com/posts/owasp-genai-security-project-gets-update-new-tools-matrix/Threat Level: MEDIUMLLM SecurityAgentic AIRegulatoryIndustry NewsResearchAML.T0051 - LLM Prompt InjectionAML.T0054 - LLM JailbreakAML.T0057 - LLM Data LeakageAML.T0047 - ML-Enabled Product or ServiceAML.T0056 - LLM Meta Prompt ExtractionAML.T0010 - ML Supply Chain CompromiseOWASP has updated its GenAI Security Project to formally recognise 21 generative AI risks, releasing a new tools matrix to help organisations structure their defences. The update notably distinguishes between securing traditional GenAI systems and the emerging attack surface presented by agentic AI architectures. This guidance represents a significant standards-level acknowledgement that agentic AI requires its own dedicated security posture.https://gridthegrey.com/posts/openai-impacted-by-north-korea-linked-axios-supply-chain-hack/Tue, 14 Apr 2026 07:39:02 +0000https://gridthegrey.com/posts/openai-impacted-by-north-korea-linked-axios-supply-chain-hack/Threat Level: HIGHSupply ChainIndustry NewsLLM SecurityAML.T0010 - ML Supply Chain CompromiseAML.T0047 - ML-Enabled Product or ServiceOpenAI has been impacted by a supply chain attack attributed to North Korea-linked threat actors, involving a compromised macOS code signing certificate associated with the Axios JavaScript library. The incident highlights the vulnerability of major AI platforms to upstream software supply chain compromises, which could expose users to malicious code distributed through trusted tooling. As a leading AI infrastructure provider, any compromise of OpenAI's build or distribution pipeline carries significant downstream risk for enterprises relying on its models and APIs.https://gridthegrey.com/posts/python-supply-chain-compromise/Mon, 13 Apr 2026 15:41:27 +0000https://gridthegrey.com/posts/python-supply-chain-compromise/Threat Level: HIGHSupply ChainLLM SecurityIndustry NewsAML.T0010 - ML Supply Chain CompromiseAML.T0018 - Backdoor ML ModelAML.T0047 - ML-Enabled Product or ServiceA malicious supply chain attack was discovered in litellm version 1.82.8, a widely-used Python library that serves as a unified interface for interacting with large language model APIs. The compromised package contained a hidden .pth file executing arbitrary code on every Python interpreter startup, meaning any developer or AI system relying on litellm could be silently compromised without triggering an explicit import. Given litellm's central role in LLM-powered application stacks, this attack vector poses significant risk to AI pipeline integrity, credential theft, and downstream model infrastructure.https://gridthegrey.com/posts/over-1000-exposed-comfyui-instances-targeted-in-cryptomining-botnet-campaign/Mon, 13 Apr 2026 14:44:56 +0000https://gridthegrey.com/posts/over-1000-exposed-comfyui-instances-targeted-in-cryptomining-botnet-campaign/Threat Level: HIGHSupply ChainIndustry NewsLLM SecurityAML.T0010 - ML Supply Chain CompromiseAML.T0047 - ML-Enabled Product or ServiceAML.T0040 - ML Model Inference API AccessThreat actors are actively exploiting internet-exposed ComfyUI instances — a popular AI image generation platform — by abusing its custom node execution feature to achieve unauthenticated remote code execution. Over 1,000 publicly accessible instances have been identified as targets, with compromised hosts enrolled in Monero and Conflux cryptomining operations and a Hysteria V2 proxy botnet. The attack highlights critical supply chain and insecure plugin design risks inherent in AI/ML tooling ecosystems.https://gridthegrey.com/posts/google-s-vertex-ai-is-over-privileged-that-s-a-problem/Mon, 13 Apr 2026 14:39:34 +0000https://gridthegrey.com/posts/google-s-vertex-ai-is-over-privileged-that-s-a-problem/Threat Level: HIGHAgentic AILLM SecurityResearchIndustry NewsAML.T0051 - LLM Prompt InjectionAML.T0057 - LLM Data LeakageAML.T0040 - ML Model Inference API AccessAML.T0047 - ML-Enabled Product or ServiceAML.T0012 - Valid AccountsPalo Alto Networks researchers have identified over-privilege vulnerabilities in Google's Vertex AI platform, demonstrating how malicious actors could exploit AI agents to exfiltrate sensitive data and pivot into restricted cloud infrastructure. The findings highlight systemic risks in agentic AI deployments where excessive permissions granted to AI workloads expand the attack surface beyond traditional cloud security boundaries. This research underscores the growing urgency around securing AI agent permissions and enforcing least-privilege principles in enterprise ML platforms.https://gridthegrey.com/posts/flowise-ai-agent-builder-under-active-cvss-10-0-rce-exploitation-12000-instances/Mon, 13 Apr 2026 14:19:20 +0000https://gridthegrey.com/posts/flowise-ai-agent-builder-under-active-cvss-10-0-rce-exploitation-12000-instances/Threat Level: CRITICALAgentic AILLM SecuritySupply ChainIndustry NewsAML.T0047 - ML-Enabled Product or ServiceAML.T0040 - ML Model Inference API AccessAML.T0010 - ML Supply Chain CompromiseA maximum-severity (CVSS 10.0) remote code execution vulnerability in Flowise, a widely-used open-source AI agent builder, is under active exploitation with over 12,000 internet-exposed instances at risk. The flaw, CVE-2025-59528, exists in the CustomMCP node and allows unauthenticated JavaScript execution with full Node.js runtime privileges via unsanitised MCP server configuration input. This marks the third Flowise vulnerability exploited in the wild, underscoring systemic security gaps in AI orchestration and agent-building platforms.https://gridthegrey.com/posts/how-we-broke-top-ai-agent-benchmarks-and-what-comes-next/Sat, 11 Apr 2026 19:15:56 +0000https://gridthegrey.com/posts/how-we-broke-top-ai-agent-benchmarks-and-what-comes-next/Threat Level: CRITICALAgentic AIAdversarial MLResearchLLM SecurityAML.T0043 - Craft Adversarial DataAML.T0031 - Erode ML Model IntegrityAML.T0047 - ML-Enabled Product or ServiceAML.T0051 - LLM Prompt InjectionAML.T0015 - Evade ML ModelResearchers at UC Berkeley demonstrated that every major AI agent benchmark — including SWE-bench, WebArena, OSWorld, and others — can be fully exploited to achieve near-perfect scores without solving a single task, using trivial environmental manipulation rather than genuine capability. The attacks include pytest hook injection, config file leakage, DOM manipulation, and reward component bypassing, with zero LLM calls required in most cases. This represents a systemic integrity failure in the evaluation infrastructure underpinning AI deployment decisions across industry and research.https://gridthegrey.com/posts/anthropic-claude-mythos-preview-the-more-capable-ai-becomes-the-more-security-it/Sat, 11 Apr 2026 09:21:26 +0000https://gridthegrey.com/posts/anthropic-claude-mythos-preview-the-more-capable-ai-becomes-the-more-security-it/Threat Level: LOWLLM SecurityAgentic AIIndustry NewsRegulatoryAML.T0047 - ML-Enabled Product or ServiceAML.T0051 - LLM Prompt InjectionAML.T0040 - ML Model Inference API AccessCrowdStrike, as a founding member of Anthropic's Mythos program, is highlighting the security challenges posed by increasingly capable frontier AI models, signaling a growing industry focus on securing agentic and large-scale AI systems. The article underscores the philosophical and practical position that AI capability gains must be matched by proportional security investment. While the piece is primarily a vendor partnership announcement and executive viewpoint, it reflects an important industry trend toward formalising AI-specific security frameworks and tooling.