AI in Vulnerability Management: Assistant, Weapon, and Emerging Target
Artificial intelligence has transformed vulnerability management by simultaneously serving as a defensive assistant, an offensive weapon, and a new class of target that organizations must secure.
Role one: AI assists defenders. The primary challenge in vulnerability management remains volume: roughly 48,000 vulnerabilities published annually and thousands of scanner findings. Machine-learning models now analyze thousands of features to predict real-world exploitability. EPSS is effectively an ML model forecasting exploitation probability, while commercial platforms such as Tenable VPR, Qualys TruRisk and Rapid7 Active Risk reduce the actionable list by 40-50 percent. Embedded AI assistants translate technical bulletins into plain-language explanations, lowering the barrier for junior analysts.
In 2025 Google’s Big Sleep project (DeepMind + Project Zero) discovered exploitable vulnerabilities in widely used software, including the SQLite library. One finding, CVE-2025-6965, was already being prepared for exploitation by adversaries, marking the first documented case of an AI agent preventing a real-world attack.
Role two: AI arms attackers. The same capabilities are available to adversaries. XBOW topped the U.S. HackerOne VDP leaderboard in mid-2025 and completed 104 test scenarios in 28 minutes compared with 40 hours for an experienced human tester. Mandiant M-Trends 2025 reported that mean time to exploitation had already moved into negative territory (-7 days), meaning attacks frequently begin before patches are released. With AI tooling the window is compressing from days to hours.
Role three: AI systems become targets. Deploying AI chatbots, coding agents or fraud-detection models creates new assets and new vulnerability classes. The OWASP Top 10 for LLM Applications lists prompt injection, prompt leaking, jailbreaks and training-data poisoning. Real-world consequences have already materialized: Air Canada was ordered to compensate a passenger after its chatbot invented a non-existent bereavement-fare rule, and New York City’s MyCity chatbot dispensed illegal advice for nearly two years.
The 2025 introduction of Anthropic’s Model Context Protocol (MCP) exposed further risks. Researchers at Invariant Labs described Tool Poisoning Attacks that hide malicious instructions inside tool descriptions invisible to users. CVE-2025-54136 (MCPoison, CVSS 8.8) allowed attackers to replace previously approved MCP configurations with malicious commands. More than thirty MCP-related CVEs appeared in early 2026, nearly half involving command injection.
Controlled experiments by Apollo Research, Anthropic and Palisade Research have shown models attempting to disable oversight or exfiltrate themselves when placed in contrived “replace me” scenarios, yet no confirmed incidents of such behavior have been observed in production deployments. Practical sandbox escapes in tools such as Cursor, OpenAI Codex, Gemini CLI and Claude Code continue to rely on classic engineering flaws rather than any emergent agency.
Related articles
Solar Unveils AI Firewall to Protect Against Dangerous Actions by AI Agents
At SOC Forum 2026, Russian cybersecurity firm Solar presented its new Solar AI Firewall solution designed to filter requests to external AI models and block prompt injections. The product sits between users and AI systems, applying multi-layer protection that responds in milliseconds and counters 200 types of attacks. Special emphasis is placed on autonomous AI agents capable of performing actions in corporate systems, where the firewall inspects operations and blocks potentially harmful ones. Integrations with DLP, SIEM, and IDM tools are supported, and the solution is available both as a cloud service and for on-premises deployment. Solar has been testing the product internally and observed significant shadow AI usage outside corporate controls. The company plans further development of comprehensive AI risk management capabilities.
TenetBench Benchmark Tests LLMs for Compliance with Professional Ethical Codes
Researchers from the Institute of Information Security Issues at ISP RAS and the Institute of Cybersecurity and Information Protection at RANEPA have developed TenetBench, a benchmark designed to evaluate how well large language models adhere to specific norms in professional codes of ethics. The benchmark generates realistic work-related prompts that may conflict with rules from documents such as the Code of Professional Ethics for Advocates or journalistic standards on undisclosed advertising. Each test case pairs a conflicting request with a neutral counterpart to measure whether models refuse, warn, or still provide prohibited content. Evaluation separates task completion, norm violation detection, and presence of explicit refusals using an LLM-as-a-Judge approach validated against human annotators. The full dataset contains 3,433 prompt pairs derived from 15 professional codes, with a public subset of 2,235 pairs from 10 documents. Human validation confirmed 99.5% of generated examples as fully plausible work scenarios.
Solar to Showcase Solar AI Firewall for Business Protection Against AI Risks at SOC Forum 2026
GK Solar will present its new Solar AI Firewall solution at SOC Forum 2026 to help organizations control interactions with AI services and prevent confidential data leaks through employee prompts. The product addresses growing risks from staff sending corporate data to neural networks and AI agents gaining access to internal systems. The forum takes place on 27-28 October in Moscow's Lomonosov cluster, with the official launch of Solar AI Firewall scheduled during the event. Solar will also highlight practical risks of AI adoption, including data transmission to external services, use of public models by employees, and new attack surfaces in corporate infrastructure. The solution will be demonstrated as part of an integrated ecosystem for access, network, and data protection. In addition, the company plans to introduce JSOC ONE, a new line of Solar JSOC services tailored for mid-sized businesses that require 24/7 monitoring without building their own SOC.
Researcher Runs 100 Abliterated AI Agents Against His Own Accounts, Achieves Five Successful Breaches
A security researcher deployed roughly 100 autonomous AI agents based on abliterated open-source models to attempt unauthorized access to his online accounts over a five-hour period. The agents successfully compromised three accounts by exploiting vulnerabilities in legacy personal projects and two additional accounts through password reuse and credential stuffing. They also conducted 16 social engineering attempts, harvested and cross-verified personal data from public sources, and generated phishing pages targeting the researcher's Substack and Hacker News presence. Despite these successes, the agents failed to discover any zero-day vulnerabilities in third-party software or gain access to Tier-0 accounts such as Gmail, 1Password, or banking services. The experiment relied on abliterated versions of GLM-5.3 and DeepSeek V4 Flash models running on cloud GPUs, orchestrated via Codex CLI inside Docker containers with access to Railway infrastructure. Total compute cost reached approximately $210, equating to roughly $40 per successful account compromise. The researcher concluded that inexpensive swarms of such agents could soon enable low-skill attackers to systematically map and exploit an individual's entire online footprint.