OWASP

What Server Logs Reveal About Attackers: Analyzing Suspicious Requests on Linux

Server logs provide critical insights into potential intrusions by recording events from the operating system, SSH authentication, and web applications. The guide explains how to examine Linux system journals with journalctl, review authentication logs on Debian and RHEL systems, and inspect Nginx access logs for unusual HTTP requests. Analysts learn to identify patterns such as rapid failed login attempts using multiple usernames from a single IP address, which may indicate brute-force or automated scanning activity. The article also covers counting the most active IP addresses with awk and sort commands while stressing the need to compare findings against normal traffic baselines. Additional recommendations include logging structured security events in JSON format with fields for timestamp, user ID, resource, and outcome, following OWASP guidelines. The text warns against storing sensitive data like passwords in logs and highlights limitations such as possible log tampering or unsynchronized server clocks. A practical seven-step investigation plan helps responders preserve evidence and verify hypotheses before taking action.

Troubleshooting AI Bug Hunting: Seven Critical Mistakes That Destroy Report Validity

Two Russian researchers shared detailed lessons from six months of running AI agents on domestic bug bounty platforms. The presentation, originally delivered at OFFZONE, covers hallucinations, lack of validation, threat model misunderstandings, severity inflation, bloated reports, high token costs, and scope violations. Vladislav Bek and Daniil Shushakov describe practical fixes including tool-enabled agents, dual-agent verification, strict templates, and cascaded model architectures. They emphasize that AI must execute real commands in isolated environments rather than fabricate results. The speakers also address scaling on private events, handling model refusals, and the continued necessity of human final validation. Their checklist stresses business context, proven impact, and staying within program boundaries to produce accepted, high-value reports.

Research Exposes Limitations in AI Skill Scanners for Agent Vulnerability Detection

An independent researcher evaluated three local vulnerability scanners for AI agent skill packages without using LLMs or external APIs. The test used 12 synthetic packages containing both benign and malicious instructions, including attempts to bypass consent via sub-agents and hidden instructions in nested files. Cisco Skill Scanner, NVIDIA SkillSpector, and AgentVerus were run in isolated containers. All three tools missed a critical delegation bypass in package 10, while correctly flagging an educational attack citation in package 5 as High severity. NVIDIA SkillSpector performed best on nested file detection, and the findings directly shaped the integration of a lightweight static analyzer plus optional NVIDIA scans into the CEO AI project. The study also highlighted the need to preserve detailed findings rather than relying solely on final safety scores.

Information Flow vs Code: The Blind Spot in AI Security

The rapid adoption of AI-generated text is creating a systemic instability in the information environment that trains large language models. As synthetic content proliferates and models consume their own outputs across generations, research shows measurable degradation in output quality even when code and tests continue to function normally. Detectors and models including Aidetector, ZeroGPT, GPTZero, Claude, ChatGPT, Grok, Gemini, DeepSeek and Meta AI produce inconsistent verdicts on the same human-written text, with some labeling classical rhetorical devices as AI markers. All tested models immediately offered to "humanize" the content, accelerating the very loop that pollutes training data. The article demonstrates that Tolstoy, Cervantes, Proust, Hemingway, Gogol and even fragments of the US Constitution have been flagged as AI-generated by current detectors. This feedback loop threatens the reliability of future AI agents that rely on external information flows rather than isolated code safeguards.

AI Agents Given Code and API Access Can Now Assist Attackers

An AI assistant that only answers questions can make mistakes, but an AI agent with access to email, code execution, corporate APIs and internal data can make those mistakes inside production infrastructure. The difference is fundamental: once tools, credentials and internal data are connected to the model, it becomes a privileged user that may not distinguish legitimate commands from hidden instructions on a web page. OWASP lists prompt injection, sensitive data disclosure, unsafe output handling and excessive autonomy as key risks for LLM applications. MITRE ATLAS specifically describes techniques involving prompt injection, context poisoning and tool invocation by AI agents. The article examines how agents differ from chatbots, how attackers can control them through untrusted content, and why a system prompt alone cannot protect code, data and APIs. CyberED is running its free NeuroAugust series of events and materials on AI in cybersecurity, including a session on secure AI system development.

Prompt Injection Emerges as Top Risk for LLM Applications in Production

Prompt injection attacks are moving from theoretical demonstrations to real-world exploits targeting AI assistants in enterprise environments. Attackers embed malicious instructions in emails, documents, and code comments that override developer rules when models process untrusted input. Incidents involving Microsoft 365 Copilot, GitHub Copilot, and Cursor have shown data exfiltration and remote code execution risks with severity scores above 9.0. The core issue stems from the lack of strict boundaries between trusted system prompts and untrusted external content fed into large language models. Defenses require layered controls including code-enforced permissions, input filtering, human confirmation for high-risk actions, and explicit marking of external data. Major vendors including OpenAI, Anthropic, and Google acknowledge that no single static defense can fully eliminate the threat. OWASP ranks prompt injection as the leading risk for LLM applications, urging organizations to treat AI agents as systems with untrusted inputs.

Bots Now Form Over Half of Global Internet Traffic in 2025, Driving API Attacks and Business Metric Distortion

Automated clients generated more than 50% of analyzed internet traffic in 2025, with malicious bots responsible for 40% of the total volume. AI-enabled automation attacks increased 12.5 times year-over-year, while daily API attacks rose 113% according to Akamai data. Simple scripts still dominate volume at 59% of bot traffic, yet sophisticated botnets exceeding 4.5 million devices now distribute activity across residential proxies and compromised endpoints. Credential stuffing, scraping, and transaction abuse continue to target business logic rather than software vulnerabilities, distorting analytics, inflating infrastructure costs, and degrading user experience. Cloudflare reports that 20% of verified bot traffic now comes from AI crawlers, blurring lines between beneficial and harmful automation. Organizations must classify bots by intent, delegation, and business impact instead of relying on IP reputation or single signals such as User-Agent strings.

Pilcrow Publishes Free Comprehensive Guide on Web Authentication

Author Pilcrow has released a detailed, ad-free handbook covering authentication and authorization practices for web applications. The resource draws on personal experience and includes practical examples in JavaScript and Go. It examines password-based methods, email verification, multi-factor authentication, passwordless flows, and passkeys while highlighting associated risks and usability trade-offs. The book also provides in-depth guidance on session management, token security, expiration policies, and email address handling as account identifiers. Recommendations emphasize choosing methods that match application security requirements and user expectations. Additional context is given on OWASP resources and community support channels.