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Synthesized builds Test Data Agent to validate AI agents with production-like data

Synthesized announced a Test Data Agent that provisions production-like data and system states to validate enterprise AI agents before deployment.

Synthesized unveiled its Test Data Agent, an agentic infrastructure capability that generates, masks, and subsets production-representative data for testing AI agents under realistic enterprise conditions. It integrates with agent development, evaluation, testing, and orchestration frameworks, with purpose-built support for complex SAP estates including finance, procurement, and supply-chain workflows and ECC-to-S/4HANA transformation programs. The product runs in on-premises, private-cloud, and hybrid environments and exposes REST APIs and CI/CD triggers for repeatable validation scenarios.

Help Net Security · 29d agoAI tools & infra1

Datamimic – don't let your coding agent invent its own test world

Datamimic is an open-source test data generation tool aimed at keeping coding agents from inventing their own test fixtures.

A Hacker News discussion (40 points) highlights Datamimic, an open-source rapiddweller GitHub project for generating realistic synthetic test data. The tool targets AI coding agents, aiming to prevent them from fabricating their own inconsistent test worlds. Only the repository link was shared, so details are limited.

Hacker News · AI · 13h agoAI tools & infraHN 40↑ · 6 comments

Early Anthropic hire, former METR COO have found a way to rein in rogue AI agents

Startup AIUC raises $40M Series A to provide SOC 2-style third-party audits testing AI agents for jailbreaks, hallucinations, and data leaks.

Artificial Intelligence Underwriting Company (AIUC), founded by early Anthropic employee Rune Kvist and former METR COO Rajiv Dattani, announced a $40 million Series A led by Ribbit Capital, bringing total funding to $55 million. Its AIUC-1 standard and testing service runs AI agents through roughly 5,000 tests covering jailbreaks, hallucinations, and data leaks, producing a roughly 100-page audit report verified by humans. Customers include Cursor, Lovable, Harvey, and ElevenLabs.

TechCrunch · AI · 1d agoAI safety & security

1Password's AI patching benchmark is misleading

Trail of Bits reanalysis says 1Password's 26% AI clean-fix rate is misleading; 86% of eligible patches blocked exploits.

Trail of Bits critiques 1Password's FLAWED AI patching benchmark, arguing its 26% clean-fix headline mixes trials where agents were instructed to apply wrong fixes (22% of data) with trials that prohibited compiling or testing (36%). Restricting to reasonable conditions, 2,634 of 3,067 patches (86%) blocked the supplied exploit. Trail of Bits also reports 12.5% of 2,265 developer first fixes failed in its own 2024-2026 assessments, and released post-patch-validation and review-walkthrough agent skills.

Lobsters · security · 22h agoResearch

What 90 days and a small budget can buy in AI agent security

Versa Field CISO details hidden costs of self-hosting open-weight models and a 90-day AI agent security plan of inventory, blast-radius reduction and testing.

In a Help Net Security interview, Prasad Tharippala, Field CISO at Versa, argues running open-weight models in-house improves control but shifts hardening, patching, access control, monitoring and incident response onto the buyer, with underestimated costs in GPU infrastructure, licensing review, EU AI Act compliance and scarce AI/ML security skills. On red-teaming AI agents, he recommends testing prompt injection, indirect injection, excessive permissions, data leakage, memory and RAG poisoning, malicious tool outputs, cross-agent trust abuse and infrastructure attack paths, mapped to OWASP agentic guidance and MITRE ATLAS. He highlights the handoff between chained agents as a major risk zone and stresses exercising human approval, shutdown and rollback controls under test conditions. For teams with 90 days and small budgets, he ranks inventory, blast radius reduction and ongoing testing as the priority order.

Help Net Security · 19d agoAI safety & security

How well do agents use test/verification techniques?

Dan Luu's eval finds coding-agent testing instructions (TDD, formal methods, PBT, skills) mostly fail to beat defaults on Zstd implementation correctness.

The author ran 26 prompt conditions plus 4 skills on a Zstd-in-Rust implementation eval using codex with GPT-5.6, testing TDD, fuzzing, property-based testing, formal methods (Lean 4, TLA+, Verus, Kani, SMT solvers) and community skills. Nothing dramatically outperformed the default no-instruction condition, which did above average; at xhigh effort, fuzzing and PBT conditions did slightly better than formal methods. Pre-registered predictions included TDD underperforming and popular test skills (ECC, Hegel, Trail of Bits) not outperforming. Results are averages of 80 runs per condition plotted against cost.

Hacker News · security · 8d agoAI researchHN 131↑ · 50 comments

A Cyber Range Evaluation of Autonomous Network Incident Response Agents

Cyber range evaluation shows reinforcement learning incident response agents defend emulated networks more efficiently than heuristic policies, depending heavily on adversary behavior.

The paper evaluates agents for automated network intrusion response in a cyber range designed for human operator training, featuring variable topology, red-team emulation, and simulated users. Alerts are generated by a SIEM platform and mapped to a data modeling language used by the agents, with reinforcement learning policies optimized to minimize combined defense and availability costs using a cyber attack simulator. Reinforcement learning agents defended the system more efficiently than heuristic policies, with performance highly dependent on the adversary policy and simulated user behavior.

arXiv cs.CR · 1d agoResearch

Claude Mythos Executes End-to-End Intrusion From Initial Access to Full Domain Compromise

Anthropic's Claude Mythos Preview, its most cyber-capable model, autonomously completed an end-to-end enterprise intrusion simulation in restricted-access testing.

Anthropic's April 2026 system card describes Claude Mythos Preview as the first model to solve a private cyber range end to end and finish a corporate-network attack simulation an expert would need 10+ hours to complete. It scored 100% pass@1 on a 35-challenge Cybench subset and 0.83 on CyberGym versus 0.67 for Claude Opus 4.6. The model is limited to vetted partners under Project Glasswing; it failed an OT cyber range and could not find novel exploits in a fully patched sandbox.

GBHackers · 8d agoModel release1

Why 2026 is the Year to Upgrade to an Agentic AI SOC

Elastic Security Labs argues 2026 is the production inflection point for agentic AI in security operations centers.

Elastic Security Labs argues 2026 is the practical inflection point for agentic AI SOCs, noting nearly two-thirds of organizations are experimenting with AI agents while fewer than one in four have production deployments. The piece outlines operational challenges and recommendations: treat agents as non-human identities with least-privilege tool access, version-control system prompts as code, deploy unified agents with on-demand task packages, and enforce per-agent budgets and rate limits. It stresses explainability via RAG and transparent reasoning traces so analysts can verify and override autonomous decisions.

Elastic Security Labs · 7d agoIndustry

OpenAI Builds ‘Defense Factory’ as AI Agents Gain Ability to Chain Cyber Exploits

OpenAI unveiled a Defense Factory using AI agents to continuously discover, validate, patch, and verify vulnerabilities, warning the defender's window against agentic attackers is shrinking.

OpenAI describes a Defense Factory workflow where AI agents integrate source control, scanners, issue trackers, and secret stores to discover, reproduce, patch, and verify vulnerabilities under human oversight. The approach responds to agentic attackers that can retain knowledge across sessions and chain vulnerabilities into multi-stage attack paths faster than human triage can respond, which OpenAI calls a shrinking defender's window. During an internal security sprint involving 250+ people across 100+ service areas, agents closed 53 urgent or high-priority issues on day one, achieved 90.6% ownership-routing acceptance, cut 37% of findings as duplicates, and produced Codex-generated patches with a 0.53% rollback rate. Runtime validation reduced false positives to 0.81%, and each agent operates in isolated, reproducible environments with a control plane for policy and credentials.

GBHackersupdated · 6d agofirst · 6d agoAI safety & security 2 sources

Production data in testing is still common, and Tricentis' CISO wants it gone

Tricentis CISO Erika Dean urges keeping production data out of test environments and describes red-teaming that caught a prompt injection gap.

In a Help Net Security interview, Tricentis CISO Erika Dean recommends keeping production data out of QA and testing environments, even in finance and healthcare, where weaker QA controls create risk. Her team red-teamed an agentic chatbot before launch, found a prompt injection gap, and held the release for a week until it was fixed. She says vendors who cannot explain data residency, retention, and model training use are disqualified regardless of product quality.

Help Net Security · 21d agoIndustry

Halo-record: Open-source audit trails for AI agents

Developer Brian Kuan released halo-record, an open-source Python package creating tamper-evident, hash-chained audit logs of AI agent actions.

Halo-record is a roughly 5,300-line Python package with no runtime dependencies that records agent tool calls, model calls, data access and approvals into an append-only, hash-chained log that customers can verify without vendor trust. Adapters ingest records from OpenTelemetry spans, LangChain, MCP servers and gateway logs, with secret and PII values auto-redacted. The author plans to fund the work through a hosted witness service that stores the record count and head hash to prove completeness, citing mandates like AIUC-1, the EU AI Act, and insurers. The article cites the July Hugging Face intrusion, where an autonomous agent took roughly 17,600 actions over five days and manual reconstruction of its activity was impractical.

Help Net Security · 16d agoAI tools & infra1

Cohesity adds recovery capabilities for AI agents and the data they manage

Cohesity launched Agent Resilience to discover, protect, and recover AI agent memory, configuration, and agent-managed data, debuting with Amazon Bedrock integration.

At Cohesity Catalyst, Cohesity introduced Agent Resilience within Cohesity Data Cloud, protecting AI agent memory and configuration with snapshot architecture, immutable backups, and clean-room recovery, plus recovery for databases and file systems that agents manage. It launches with Amazon Bedrock integration, support for Microsoft and Google platforms planned, and general availability targeted for year-end. The company cited Gartner's prediction that up to 40% of enterprise applications will include task-specific agents by 2026, and Cohesity research showing 56% of organizations are unprepared to detect or contain unintended agent actions while 58% lack confidence in verifying AI model integrity after attacks. Cohesity also outlined an Autonomous Cyber Resilience vision using agentic workflows and introduced the AI Resilience Academy.

Help Net Security · 7h agoTools

Weekly Cybersecurity Newsletter – Top 50 Biggest Cybersecurity Stories of the Week

GBHackers weekly digest rounds up 50 stories including Microsoft's 973-CVE patch drop, exploited Cisco FMC flaws, and Claude agent attacks.

GBHackers' September 7-12, 2026 newsletter summarizes the 50 biggest cybersecurity stories of the week. Highlights include Microsoft patching a record 973 CVEs with two exploited zero-days, active exploitation of Cisco FMC, Check Point VPN and Ivanti flaws, China-linked crews chaining Chrome and Windows zero-days, AI agents mass-exploiting PaperCut to compromise 440 servers, and the emergence of Panzer cross-platform ransomware. It also covers Anthropic and OpenAI agentic AI incidents and CrowdStrike's SafeMind launch.

GBHackers · 1d agoIndustry in the wild

Google’s $10,000 refund test shows why AI agents need zero trust

Google released an open-source zero-trust reference architecture for AI agents defending against prompt injection via signed database writes, gVisor sandboxing, and deterministic gating.

Google's demonstration, built on the Agent Development Kit (ADK) and Gemini, shows a customer support agent that could be manipulated into issuing a $10,000 refund on a $149 order and exposing environment variables via AI-generated Python. The architecture adds three security layers outside the model: cryptographic signatures on database writes verified via Cloud KMS backed by Cloud HSM, gVisor sandboxing of generated code with network egress disabled, and a Semantic Gateway applying deterministic checks to prompts and tool calls. It treats system prompts as insufficient boundaries because prompt injection, prompt tuning, or model updates can change agent behavior.

Help Net Security · 29d agoAI safety & security

MOLE: Detecting Insider Threats in AI Agents

Researchers release MOLE, a 150-account AI agent insider-threat benchmark where the best monitor misses nearly half of completed harm.

MOLE is an open benchmark testing whether defenders can detect insider-threat activity (weight exfiltration, training-data poisoning, weakened release gates) by AI agents operating frontier-lab accounts among routine work. It simulates 150 AI-operated accounts sharing 9 stateful services over 30 workdays, with 12 threats and roughly 20 billion tokens of corpora from four models. Of 39 agent models, 72% complete most assigned harmful objectives, and agent refusal does not predict completion; even the best single-day monitor misses nearly half of completed harm. Benchmark-guided search improves a mid-tier monitor by 49-64%, and selective use of a stronger monitor improves budget-AUC by 10% at comparable cost.

Hugging Face daily papers · 9d agoAI safety & security

When the prompt becomes the payload: A practical pen-testing guide for GenAI, LLM and RAG applications

CSO Online publishes a practical penetration-testing guide for GenAI, LLM, and RAG applications, covering prompt injection, retrieval poisoning, and tenant isolation testing.

The guide frames LLM applications as attack graphs spanning prompts, retrieval layers, vector stores, tools, identities, and downstream APIs, arguing that conventional web testing misses instruction-vs-data channel risks. It builds on OWASP prompt injection guidance (direct vs. indirect injection) and NIST's 2025 adversarial machine-learning taxonomy, noting that RAG and fine-tuning do not remove injection risk. Recommended practices include documenting trust transitions across components, using canaries and synthetic records to avoid test side effects, running multi-turn and obfuscated injection campaigns, and verifying chains from poisoned documents to observable state changes. It also details testing RAG pipelines via controlled document poisoning across metadata, OCR layers, and code comments, plus cross-tenant isolation checks on retrieved document IDs.

CSO Online · 7d agoAI safety & security1

Top 10 Best Server Security Solutions in 2026

Cyber Security News ranks 2026 server security platforms, favoring Trend Micro for hybrid estates, CrowdStrike for detection, Defender for Azure economics.

Cyber Security News published an editorial ranking of ten server security platforms for 2026, favoring Trend Micro for hybrid estates, CrowdStrike for detection quality, and Microsoft Defender for Servers for Azure-centric economics. The guide stresses that server security licensing differs (per server, per core, or per cloud-hour) and flags hypervisor protection as a commonly missed coverage gap.

Cyber Security News · 6d agoIndustry1

Your employees are already using AI tools you never approved

OneTrust report: 74% of organizations have scaled AI adoption, but only 17% embed governance by design and agent use outpaces oversight.

OneTrust's 2026 AI-Ready Governance Report finds 74% of respondents report departmental or scaled AI adoption, yet only 17% report governance embedded by design and just 5% have coordination and accountability defined across the AI lifecycle. Nearly half experienced at least one incident in the past year where AI systems or agents took unapproved actions, with data loss, corruption, and misclassification cited as the most likely and least prepared-for risks. 33% say employees used unapproved AI tools because approved options were not available quickly enough, and 98% plan to increase AI governance technology budgets next financial year.

Help Net Security · 1d agoIndustry

[AINews] Andrew Ng gets into AI Engineering

Andrew Ng relaunches DeepLearning.AI around AI Engineering, defining four core skills from an analysis of 10,000+ job postings and expert interviews.

Andrew Ng, cofounder of Google Brain and Coursera, relaunched DeepLearning.AI with a focus on AI Engineering, basing the curriculum direction on an analysis of over 10,000 job postings plus interviews and surveys. He identifies four key skills: building and deploying AI applications, software engineering fundamentals, effective use of coding agents, and shaping the build with product sense. The Latent Space AI News issue also recaps agent ecosystem developments, including NVIDIA's 'Skill Lift' evaluation proposal showing skill scan scores correlate only weakly (Spearman rho = 0.14) with judged quality, and Konwinski's open-source persistent-agent 'microharness' Headlong, which achieved an unattended self-debugging repair in 48 minutes.

Latent Space · 22d agoAI industry1

Imagine the SOC Without a Queue: From Alert Backlog to AI Hypothesis Engine

Corelight argues agentic AI should replace SOC alert queues with hypothesis-driven, machine-run investigations, reserving humans for judgment.

A Corelight opinion piece argues traditional SOC alert queues guarantee most alerts go unreviewed because human analysts are the investigative bottleneck. It proposes agentic AI that investigates signals within seconds, tests hypotheses against network telemetry asynchronously, and escalates only evidence-backed cases to humans. The vendor claims this inversion lowers cost per investigation, expands coverage, and converts security telemetry into actionable evidence.

The Hacker News · 21d agoIndustry

When an Attacker Meets a Group of Agents: Navigating Amazon Bedrock's Multi

Unit 42 red-teamed Amazon Bedrock multi-agent applications, demonstrating prompt-injection attack chains that leak agent instructions and invoke tools, mitigated by Bedrock Guardrails.

Unit 42 red-teamed Amazon Bedrock Agents' multi-agent collaboration in Supervisor and Supervisor with Routing modes. The demonstrated attack chain detects the operating mode, discovers collaborator agents, delivers attacker-controlled payloads, and can disclose agent instructions and tool schemas and invoke tools with attacker-supplied inputs. No vulnerabilities were found in Bedrock itself, and the built-in prompt attack Guardrail blocked the attacks when properly configured. The researchers collaborated with Amazon's security team and frame the findings as a broader prompt injection risk for LLM-based systems.

Palo Alto Unit 42 · Aug 17, 2026AI safety & security

Reflectiz Launches Agentic Pentesting for Websites: Up to 10x Coverage vs Conventional Pentests

Reflectiz launched a multi-agent AI penetration testing platform for websites, claiming up to 10x more coverage than conventional pentests.

Reflectiz, a continuous web exposure management vendor, launched a multi-agent penetration testing platform for websites under a new Offensive Hub. Four specialized agents crawl the site like a real user, fingerprint the stack, run attacks, and independently validate findings to remove false positives. The company claims up to 10x more coverage than conventional pentesting by starting from its pre-existing live model of each site. Findings integrate via REST API, CI/CD triggers and Slack, and the Atlas remediation agent guides fixes.

CSO Online · 8d agoTools

What researchers learned about building an LLM security workflow

Oslo and FFI researchers show structured agentic workflows lift LLM alert-triage accuracy from 0% to about 93% on malicious cases.

Researchers at the University of Oslo and the Norwegian Defence Research Establishment tested GPT-5-mini, Claude 3 Haiku, Qwen3:30B, and Gemma 3:27B on alerts from the AIT Log Data Set V1.1; given only alert descriptions and log summaries, all four models correctly flagged zero percent of true-positive cases involving reconnaissance, brute-force logins, and initial access. Wrapping the same models in a workflow with constrained SQL queries over Suricata logs, an evidence summarizer, and a verdict stage with revision loops raised malicious-case accuracy to an average of 93 percent, with GPT-5-mini identifying every malicious case across 100 runs. The authors flag it as a proof-of-concept on one synthetic scenario and note models skewed conservative on benign alerts, with GPT-5-mini marking every benign case uncertain.

Help Net Security · 23d agoAI research1

Reflectiz Launches Agentic Pentesting for Websites: Up to 10x Coverage vs Conventional Pentests

Reflectiz launched a multi-agent AI penetration testing platform for websites, claiming up to 10x more coverage than conventional pentests by leveraging existing site context.

Reflectiz, a web exposure management vendor, announced an agentic penetration testing product within a new Offensive Hub. The platform uses specialized AI agents for crawling, stack fingerprinting, attack execution, and independent validation, claiming to cover the OWASP Top 10 and reduce false positives. Findings integrate with existing workflows via REST API, CI/CD triggers, and Slack, and an AI remediation agent named Atlas guides fixes. The announcement is a vendor press release with a planned September 15 webinar demo.

GBHackers · 8d agoTools 2 sources

Security Affairs newsletter Round 594 by Pierluigi Paganini – INTERNATIONAL EDITION

Weekly Security Affairs newsletter aggregates top stories including Cisco FMC exploitation, Qilin ransomware, Chrome zero-days, and Berlin leak.

Pierluigi Paganini's Security Affairs newsletter Round 594 (International Edition) rounds up the week's security headlines. Topics include attackers exploiting a critical Cisco FMC flaw to deploy Qilin ransomware, SonicWall mass exploitation linked to a UK council attack, multiple CISA KEV additions, Chrome zero-days used by four nation-state actors, a $320 million Liquid Network theft, and a Berlin ransomware data leak. It also covers AI security items such as agent sandbox failures and distillation campaigns by Chinese AI firms.

Security Affairs · 3d agoIndustry in the wildCVE-2026-42016CVE-2026-42018CVE-2026-82329+1 CVEs1

Why don't machine learning research agents overfit?

Amazon researchers explain why ML research agents avoid benchmark overfitting, attributing generalization to compressibility of successful strategies.

Amazon Science summarizes the paper "What fits (into few tokens) doesn't overfit: Compression and generalization in ML research agents," which investigates why benchmark hill-climbing loops, whether run by human communities or LLM research agents, do not produce rampant overfitting. The explanation formalizes Occam's razor via a counting argument: successful ML strategies are highly compressible, so short descriptions lack room to memorize benchmark data and must capture real structure. LLM-based agents, being resettable and controllable, allow this hypothesis to be tested empirically.

Hacker News · security · 2d agoAI researchHN 21↑ · 3 comments