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AI Agents Emerge as a New Identity Class Orgs Must Secure

AI Agents Emerge as a New Identity Class Orgs Must Secure

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.2 Dark Reading

AI agents are being recognised as a distinct identity type that cannot be adequately governed using legacy service account or API token frameworks, requiring purpose-built identity and access management approaches. For defenders, this gap means agents operating today are likely over-privileged, under-monitored, and outside existing IAM policy scope. Security teams face an immediate challenge in extending least-privilege, auditability, and lifecycle management controls to autonomous agent identities before adversaries exploit the blind spot.

Prompt Injection Attacks Claude Code and Codex Execution

Prompt Injection Attacks Claude Code and Codex Execution

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 9.2 The Hacker News

Researchers at the AI Now Institute have demonstrated a proof-of-concept attack dubbed 'Friendly Fire' that tricks AI coding agents — specifically Anthropic's Claude Code and OpenAI's Codex in autonomous mode — into executing malicious binaries while performing routine security reviews. The attack embeds a disguised payload inside an open-source library and uses a plain README.md instruction to direct the agent to run a malicious shell script, bypassing existing trust-prompt defences. Because the weakness is architectural rather than version-specific, no patch exists; mitigation requires workflow changes.

Prompt Injection Attacks Manipulate AI Crypto Agents

Prompt Injection Attacks Manipulate AI Crypto Agents

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.5 SecurityWeek

Researchers identified two active campaigns embedding indirect prompt injection payloads in malicious websites to manipulate autonomous AI agents into executing unauthorised cryptocurrency transactions. The attacks exploit the growing deployment of agentic AI systems that browse the web and take real-world actions with minimal human oversight. This represents a concrete, financially motivated escalation of prompt injection from data exfiltration to direct fund theft.

Agentjacking: Prompt Injection via Malicious Bug Reports

Agentjacking: Prompt Injection via Malicious Bug Reports

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 Dark Reading

A technique dubbed 'agentjacking' exploits the inability of AI coding agents to distinguish between legitimate content and embedded instructions, allowing attackers to hijack agent behaviour through maliciously crafted bug reports. The attack represents a scalable, low-barrier prompt injection vector targeting developer workflows that rely on autonomous AI agents. As AI coding assistants gain broader adoption and elevated system permissions, this class of attack poses a significant risk to software supply chain integrity.

Token Security Publishes Agentic AI Identity Risk Analysis

Token Security Publishes Agentic AI Identity Risk Analysis

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 BleepingComputer

Token Security has published a detailed analysis of the identity and access management failures emerging as agentic AI systems proliferate across enterprise environments, highlighting how AI agents authenticate, hold credentials, and act autonomously across production systems without adequate oversight. Unlike traditional machine identities, AI agents combine human-like goal-directed behaviour with machine-speed execution, creating credential sprawl that existing IAM programs were never designed to govern. Security teams face a compounding risk: agents are being provisioned with overprivileged OAuth grants, API tokens, and cloud roles that remain unreviewed and unrevoked long after the original use case has expired.

AI Code Review Agents: DoS Loop Costs $41K in Inference

AI Code Review Agents: DoS Loop Costs $41K in Inference

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.5 Simon Willison

A hypothetical but technically grounded incident report depicts two competing AI code review agents entering an uncontrolled disagreement loop over a suspected malicious package, generating 340 comments and $41,255 in inference costs before human intervention. The scenario illustrates real risks of excessive agency, lack of circuit-breakers, and cost-based denial-of-service in multi-agent agentic pipelines. While fictional, the scenario directly mirrors documented failure modes in production AI systems and supply chain security workflows.

Google DeepMind Releases AI Agent Attack Taxonomy

Google DeepMind Releases AI Agent Attack Taxonomy

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.7 SecurityWeek

Google DeepMind researchers have released a structured taxonomy categorising adversarial attacks against autonomous AI agents into six classes — content injection, semantic manipulation, cognitive state poisoning, behavioural control, systemic, and human-in-the-loop traps — formalising an emerging threat model for agentic AI systems. For defenders, this framework codifies attack paths that exploit the agent's inability to distinguish trusted instructions from attacker-controlled data ingested from web pages, emails, documents, and tool outputs. NIST evaluation data cited in the research shows malicious instruction injection succeeded in 57% of tested agent hijacking scenarios on average, underscoring that these are active, high-yield attack vectors rather than theoretical concerns.

MoEngage Deploys Autonomous AI Agents via Aampe Acquisition

MoEngage Deploys Autonomous AI Agents via Aampe Acquisition

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 6.8 TechCrunch AI

MoEngage has acquired Aampe to deploy individualized AI agents for every customer, enabling autonomous decisions on messaging targeting, timing, and content at enterprise scale across 1,350+ brands globally. This architecture introduces a large, distributed fleet of autonomous agents operating on sensitive behavioral and PII data, dramatically expanding the blast radius of any single compromise. Security teams at enterprises adopting this platform must now reason about agent-level trust boundaries, data inference risks, and the amplification potential of adversarial manipulation across millions of simultaneous decision-making agents.

AWS Launches Bedrock AgentCore for Autonomous Payments

AWS Launches Bedrock AgentCore for Autonomous Payments

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 7.8 AWS Machine Learning Blog

AWS has launched Amazon Bedrock AgentCore Payments, a managed infrastructure layer that enables AI agents to autonomously transact with external model providers and services using the x402 payment protocol, without human intervention. This capability introduces a new class of financial attack surface where compromised or manipulated agents can autonomously spend real funds, exfiltrate value, or be redirected to malicious service endpoints. Defenders must now treat agent payment credentials and spending budgets as first-class financial controls, on par with cloud IAM policies.

AWS Launches Amazon Quick Autonomous Agents

AWS Launches Amazon Quick Autonomous Agents

FIRST LOOK ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 AWS Machine Learning Blog

AWS has shipped autonomous agents in Amazon Quick, an AI assistant that continuously executes tasks — including CRM updates, email drafting, and compliance monitoring — on behalf of users while connected to dozens of enterprise data sources and applications. This dramatically expands the attack surface for business-context compromise: a single successful prompt injection or account takeover can now translate into persistent, automated actions across an organisation's entire connected app ecosystem. Defenders must treat these agents as privileged service accounts with broad, continuous write-access, requiring dedicated monitoring, least-privilege scoping, and explicit human-in-the-loop gates for sensitive actions.

Adversa AI: 89% of AI Agents Fail Security Tests

Adversa AI: 89% of AI Agents Fail Security Tests

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.2 SecurityWeek

Adversa AI's AI Risk Quadrant report evaluated 100 AI agents across ten categories, finding that only 11 qualify as both capable and well-defended. The research identifies a structural 'power-protection inversion' where the most capable agents also present the widest attack surface, driven by a 'lethal trifecta' of private data access, exposure to untrusted content, and outbound action capability. Computer and coding agents showed the most severe exposure, raising urgent concerns about autonomous agent deployment in enterprise environments.

SentinelOne Warns on Prompt Injection Risks in AI Agents

SentinelOne Warns on Prompt Injection Risks in AI Agents

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.5 SentinelOne Blog

SentinelOne has published guidance on securing agentic AI systems, framing unverified trust in AI agents as a core enterprise risk. The piece promotes their Prompt Security product as a control layer for AI tools, agents, and pipelines deployed across the enterprise. While primarily a product-focused announcement, it highlights the genuine security challenge of blind trust in autonomous AI agents executing actions on behalf of users and systems.

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

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

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 7.2 Microsoft Security Blog

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.

Sweet Security Launches Sweet Attack Agentic AI Red Teaming

Sweet Security Launches Sweet Attack Agentic AI Red Teaming

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 7.2 SecurityWeek

Sweet Security has launched 'Sweet Attack', a continuous agentic AI red teaming platform designed to counter the growing asymmetry between AI-assisted attackers and human defenders — a tipping point the industry has termed the 'Mythos Moment'. The platform differentiates itself by grounding frontier model reasoning in live runtime telemetry from each customer's own environment, including topology, identity paths, and unencrypted Layer 7 exposure, to identify genuinely exploitable attack chains rather than theoretical ones. The development signals a broader industry shift toward autonomous, environment-aware AI agents as a necessary component of modern security operations.

Sevii Cyber Swarm Defense Token Costs Enable DoS Attacks

Sevii Cyber Swarm Defense Token Costs Enable DoS Attacks

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.5 SecurityWeek

Sevii's Cyber Swarm Defense launch highlights a structural tension in enterprise AI security: the token-based cost model of agentic AI defense becomes unpredictable and potentially unsustainable as adversarial attack volume increases. CISOs face a compounding risk where budget exhaustion mid-attack could force a fallback to understaffed human teams. The article also references Claude Mythos as a frontier model enabling higher-volume adversarial campaigns, underscoring the asymmetric cost burden between attackers and defenders.

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