Llm-Security

Agent Hijacking: Microsoft's Defense-in-Depth Framework

ATLAS OWASP MEDIUM ▲ 7.2 Microsoft Security Blog May 15, 2026

Microsoft's Security Blog introduces a layered defense-in-depth model specifically designed for autonomous AI agents, which now invoke tools, modify data, and trigger workflows with minimal human oversight. The framework identifies novel threat classes — including agent hijacking, intent breaking, and supply chain compromise — that are amplified by agentic autonomy. The guidance positions application-layer architecture, permissions, and governance as the most critical controls as agent autonomy scales.

Excessive Agency in AI Agents: Tool Access Control Gaps

ATLAS OWASP LOW ▲ 6.2 HN AI Security May 13, 2026

Statewright is an open-source framework that enforces state machine constraints on AI agents, restricting which tools agents can invoke during each phase of a workflow. The project directly addresses the Excessive Agency problem, where AI agents operating with broad, unconstrained tool access can take unintended or harmful actions. While a defensive development rather than a threat disclosure, it signals growing practitioner awareness of agentic AI risk and offers a concrete mitigation pattern for teams deploying coding agents like Claude Code, Codex, or Cursor.

CrowdStrike Red Teaming: LLM Jailbreak and Data Poisoning

ATLAS OWASP MEDIUM ▲ 6.5 SecurityWeek May 06, 2026

Joey Melo, Principal Security Researcher at CrowdStrike, outlines his methodology for AI red teaming, focusing on manipulating LLM guardrails through jailbreaking and data poisoning without altering underlying source code. His work, rooted in competitive AI hacking challenges, translates classical adversarial thinking into the emerging field of machine learning security. The profile highlights the growing professionalisation of AI red teaming as organisations seek to harden LLM deployments against real-world manipulation attacks.

AI Agents Exploit Excessive Agency to Delete Production Databases

ATLAS OWASP HIGH ▲ 6.5 Dark Reading May 02, 2026

Organisations are deploying AI agents into production environments without adequate security testing, resulting in destructive outcomes such as unintended deletion of production databases. The core risk is excessive agency granted to AI systems before trust boundaries and guardrails are established. This represents a systemic industry failure to apply basic security principles before integrating autonomous AI tooling into critical infrastructure.

Amazon Bedrock Prompt Injection Traverses Agent Hierarchies

ATLAS OWASP HIGH ▲ 8.5 Palo Alto Unit 42 Apr 23, 2026

Unit 42 researchers conducted red-team analysis of Amazon Bedrock's multi-agent collaboration framework, demonstrating how attackers can systematically exploit prompt injection to traverse agent hierarchies, extract system instructions, and invoke tools with attacker-controlled inputs. The research reveals that multi-agent architectures introduce compounded attack surfaces through inter-agent communication channels, though no underlying Bedrock vulnerabilities were identified. Properly configured Guardrails and pre-processing stages effectively mitigate the demonstrated attack chains.

Legacy Vulnerabilities Amplified by AI at Enterprise Scale

ATLAS OWASP HIGH ▲ 6.5 Dark Reading Apr 18, 2026

The article argues that AI's primary security risk lies not in introducing entirely new vulnerability classes, but in dramatically amplifying the impact and exploitability of well-established ones. This framing has significant implications for defenders, suggesting that legacy vulnerability management practices must be re-evaluated through an AI-augmented threat lens. The convergence of classic weaknesses with AI capabilities raises the baseline risk profile for organisations deploying or adjacent to AI systems.

Agentic AI Excessive Agency Bypasses Security Testing

ATLAS OWASP MEDIUM ▲ 6.5 The Hacker News Apr 16, 2026

The 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.

Anthropic MCP Supply Chain Flaw Enables Command Injection

ATLAS OWASP CRITICAL ▲ 9.1 SecurityWeek Apr 16, 2026

A 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.

AI Agent Prompt Injection and Data Leakage Threats Rise

ATLAS OWASP MEDIUM ▲ 6.5 SecurityWeek Apr 16, 2026

Capsule 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.

Anthropic Model Exploits Zero-Days Faster Than SOC Response

ATLAS OWASP HIGH ▲ 7.2 The Hacker News Apr 14, 2026

The 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.

Anthropic Claude Prompt Injection Enables Excessive Agency

ATLAS OWASP LOW ▲ 6.2 CrowdStrike Blog Apr 11, 2026

CrowdStrike, 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.

CrowdStrike Charlotte AI Vulnerable to Prompt Injection

ATLAS OWASP MEDIUM ▲ 6.5 CrowdStrike Blog Apr 06, 2026

CrowdStrike's Charlotte AI AgentWorks introduces an agentic security ecosystem where autonomous AI agents collaborate to perform security operations tasks with reduced human intervention. The platform raises important considerations around excessive agency, trust boundaries between agents, and the attack surface introduced by interconnected AI systems in security-critical environments. As agentic SOC architectures proliferate, the security of the AI agents themselves becomes a primary concern.

Claude Source Code Leak Reveals AI Supply Chain Risk

ATLAS OWASP HIGH ▲ 8.2 Dark Reading Apr 03, 2026

A reported source code leak affecting Claude, Anthropic's large language model, underscores systemic weaknesses in AI software supply chains and the absence of robust oversight mechanisms at critical development and distribution layers. The incident highlights how proprietary model code, training pipelines, and system prompts can become high-value targets for adversarial actors seeking to enable model theft, backdoor insertion, or competitive intelligence gathering. This event serves as a broader warning about treating AI development infrastructure with the same rigor applied to other critical systems.

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