Red Teaming Tactics for Cybersecurity in 2025

Hiding in the AI Traffic: Abusing MCP for LLM-Powered Agentic Red Teaming Write by Strahinja Janjuesvic1, Anna Baron Garcia2, Sohrob Kazerounian2 An article that I am putting into practice. Abstract Generative AI is reshaping offensive cybersecurity by enabling autonomous red team agents that can plan, execute, and adapt during penetration tests. However, existing approaches face trade-offs between generality and specialization, and practical deployments reveal challenges such as hallucinations, context limitations, and ethical concerns. In this work, we introduce a novel command & control (C2) architecture leveraging the Model Context Protocol (MCP) to coordinate distributed, adaptive reconnaissance agents covertly across networks. Notably, we find that our architecture not only improves goal-directed behavior of the system as whole, but also eliminates key host and network artifacts that can be used to detect and prevent command & control behavior altogether. We begin with a comprehensive review of state-of-the-art generative red teaming methods, from fine-tuned specialist models to modular or agentic frameworks, analyzing their automation capabilities against task-specific accuracy. We then detail how our MCP-based C2 can overcome current limitations by enabling asynchronous, parallel operations and real-time intelligence sharing without periodic beaconing. A case study of the Red Team Agent deployment illustrates real-world effectiveness, achieving rapid domain compromise in a stealthy manner. We furthermore explore advanced adversarial capabilities of this architecture, its detection-evasion techniques, and address dual-use ethical implications, proposing defensive measures and controlled evaluation in lab settings. Experimental comparisons with traditional C2 show drastic reductions in manual effort and detection footprint. We conclude with future directions for integrating autonomous exploitation, defensive LLM agents, predictive evasive maneuvers, and multi-agent swarms. The proposed MCP-enabled C2 framework demonstrates a significant step toward realistic, AI-driven red team operations that can simulate advanced persistent threats while informing the development of next-generation defensive systems. Link: https://lnkd.in/dqFH_Vzn

Last Friday, Anthropic launched Claude Code Security. Within hours, $15 billion evaporated from cybersecurity stocks. CrowdStrike down 8%. Okta down 9%. JFrog down 25%. The panic is understandable. An AI model just found over 500 vulnerabilities in production open-source code that had survived decades of expert review. But everyone is debating the wrong question. The question isn't whether AI can find vulnerabilities in your code. The question is: who's finding vulnerabilities in your AI? What happens when the AI itself becomes the attack surface? When prompt injections trigger unauthorized database queries through your AI-powered security tool? When a RAG pipeline leaks sensitive data that your autonomous agent was trusted to protect? Right now, most organizations deploying AI agents and autonomous tools have zero adversarial testing coverage. None. The same companies that wouldn't ship a web app without a pentest are deploying AI systems that execute queries, access internal documents, and take autonomous actions—untested. This is the gap I've been working to close. The Red Team Evaluation Framework deliberately focused on traditional adversary emulation. What it didn't cover in depth was AI—because the topic demanded its own rigorous treatment. 👉 RTEF: https://lnkd.in/ebgeTvsM That treatment is now published. 📕 AI Red Teaming: A Practical Guide to Safer AI Systems Two volumes. One core principle: test the system, not just the model. → Vol 1: Methodology. AI threat modeling, eleven attack classes, engagement lifecycle, Attacker-Target-Judge automation, the Utility Tax framework for communicating tradeoffs to business leaders. → Vol 2: Practice. Eight case studies (RAG leakage, agent privilege escalation, supply chain poisoning), vendor AI testing, risk quantification in financial terms, regulatory mappings across EU AI Act, NIST AI RMF, and ISO 42001. The real disruption isn't AI replacing cybersecurity companies. It's AI creating an entirely new attack surface that most organizations aren't testing at all. The next $15 billion won't be lost to AI finding bugs in code. It'll be lost to bugs in AI that nobody thought to look for. 🔗 https://lnkd.in/eYbUUMjb #AIRedTeaming #CybersecurityLeadership #AIGovernance #CISO #RedTeam #AISecurity #ClaudeCodeSecurity #AIRisk

Recent experiments show automated adversarial capabilities are rapidly outpacing traditional defenses. While classic security hunts for code and network flaws, LLM red teams probe the model's reasoning space. Instead of buffer overflows, we're looking at prompts that make the model ignore safety rules or reveal private training data. Traditional pen testing tools won't catch the most dangerous LLM vulnerabilities. When an LLM can invoke external functions (APIs, code execution, plugin calls), attackers can move from simple prompt injection to orchestrated system compromise. We need new testing methodologies that blend human creativity with automation. Tools like PyRIT help with coverage, but they won't replace a skilled red teamer crafting multi-turn social engineering attacks. AI red teaming hunts for ethical and safety issues that traditional pen-tests wouldn't catch. This includes probing for bias, misinformation, and privacy leaks. Testing scope must include the model's outputs AND its integration points. Every function call the model can make is an attack surface that needs validation. In OffSec, these attack techniques are evolving fast. The move now is to set up dedicated red team programs focused on AI systems—get proactive, because attackers are already working to find those gaps. What are you seeing for effective LLM security testing? What's worked (or hasn't) in your offensive testing? #Cybersecurity #RedTeaming #InfoSec

"When the AI-powered attack hit, our incident response team had no playbook and wasted 8 critical hours figuring out containment procedures..." A CISO from a Fortune 500 company shared this sobering reality with me last week. After hearing this, I discovered an incredible 2025 Cybersecurity Attacks Playbook that covers exactly what they needed - 23 critical attack vectors with complete incident response procedures. The harsh truth that CISO revealed:  - Their security team had never encountered AI-enhanced attacks before  - Existing playbooks from 2019 were completely inadequate  - Those 8 wasted hours cost them an estimated $3.2M in additional breach damage  - The attack succeeded precisely because they lacked structured response procedures Why finding this playbook feels so timely: Traditional playbooks fail against modern threats. When sophisticated AI-powered attacks hit, security teams need specific, tested procedures - not generic frameworks that leave critical response steps to improvisation. What this comprehensive resource covers: EMERGING AI THREATS → AI-Enhanced Phishing with machine-generated content → Deepfake Social Engineering targeting executives → AI-Powered Malware with adaptive evasion capabilities → Quantum Computing threats to current cryptography ADVANCED PERSISTENT CAMPAIGNS → Supply Chain Compromises through vendor infiltration → Zero-Day Exploits with custom response procedures → Advanced Ransomware with multi-stage extortion → Fileless Malware using living-off-the-land techniques INFRASTRUCTURE ATTACKS → IoT Vulnerabilities across connected device ecosystems → Cloud Security Misconfigurations exposing critical data → Rogue Access Point wireless network infiltration → Cache Poisoning and DNS manipulation attacks APPLICATION & DATA THREATS → SQL Injection with modern bypass techniques → Steganography-Based Data Exfiltration methods → Credential Stuffing at enterprise scale → Island Hopping through trusted partner networks Complete response structure per attack:  - Comprehensive preparation and asset inventory checklists  - Multi-source detection indicators across SIEM, EDR, and network tools  - Detailed IOC extraction and threat analysis procedures  - Step-by-step containment and eradication workflows  - Recovery validation ensuring complete threat elimination  - Lessons learned integration for continuous security improvement Perfect for: → Incident Response Teams avoiding costly response delays → Security Operations Centers handling sophisticated threats → CISOs preventing the experience that Fortune 500 company endured → SOC Analysts needing structured procedures for complex attacks Drop a comment below if you want access to this comprehensive resource. #CyberSecurityPlaybooks #IncidentResponse #ThreatHunting #CyberSecurity #SOC #SecurityOperations #AIThreats #Ransomware #ZeroDay #APT #InfoSec #CyberDefense #SecurityStrategy #ThreatIntelligence #CyberResilience #SecurityAutomation #CyberRisk

If you are not deep in AppSec, here is the plain-English version. AI agents are software workers. They sit between users and your systems of record. They read files, call internal services, pull data, and take actions. That makes them powerful. It also means they inherit the same security failure modes we have seen for decades in web apps. ChainLeak in Chainlit is simply the latest public example. The issues are familiar: an attacker can trick an exposed agent service into reading things it should never expose, or making outbound calls into places it should never reach. Chainlit patched in December 2025, but the broader pattern has been around for a while across agent frameworks and tool servers: fast-growing ecosystems, permissive defaults, and uneven operational hygiene. What CXOs should internalize: • This is not “AI safety.” It is basic software security, now attached to systems that have broad access and run continuously. • The biggest risk is a bridge effect. One small flaw in an agent layer can become a path into credentials, internal services, and sensitive data. • Patch notes are necessary. Architecture and controls decide whether a single bug becomes a contained event or a material incident. Executive playbook: 1. Assign ownership and measure reality Name an accountable owner for agent platforms and dependencies. Track patch posture weekly. Validate deployments, do not rely on self-reporting. 2. Reduce blast radius by default Place agent runtimes in tightly segmented networks. Restrict outbound access. Use allowlists for what agents can reach. 3. Treat credentials like high-value assets Use short-lived, scoped access. Remove long-lived keys from configs and environment variables. Centralize secrets management. 4. Add approval for irreversible actions Payments, customer record changes, privileged admin tasks, regulated data access. Make this a policy, not a team preference. 5. Operate agents like production products Continuous scanning, logging, and alerting for unusual file access, metadata probing, and unexpected outbound calls. Regular red-team style testing focused on tool misuse and supply chain risk. Bottom line: agentic systems are widening the attack surface in ways many leadership teams have not budgeted for yet. If you treat agents as privileged software that can be abused, you can keep the upside without accepting silent, systemic risk.