Written by Josh Skorich and originally posted on spektion.com
Working closely with both offensive security teams and on the front lines of runtime visibility at Spektion, I’ve seen a new approach to threat management take shape. Instead of relying on signatures or static patterns, the most effective security strategies today are grounded in real-time behavioral intelligence: insights drawn directly from how software actually runs.
This isn’t just another incremental improvement in detection technology. It’s a paradigm shift that’s exposing entirely new categories of risk and enabling proactive identification of threats that would have remained invisible under conventional approaches.
Beyond Pattern Matching: The Limitations of Traditional Detection
Most security tools today rely on what I call „signature thinking“: the assumption that threats can be identified by matching known patterns, indicators, or behaviors against predetermined rulesets. EDR solutions, for instance, excel at catching unsophisticated attacks that follow predictable patterns, but they fundamentally struggle with anything that doesn’t match their pre-configured signatures.
The problem with pattern matching isn’t that it’s ineffective – it’s that it’s incomplete. When an attacker varies their approach, uses legitimate tools in unexpected ways, or exploits previously unknown vulnerabilities, signature-based detection fails. We’re essentially playing a game where we can only recognize threats we’ve already seen.
This approach worked reasonably well when attack vectors were more predictable and the software landscape was simpler. But today’s threat environment demands something more sophisticated.
The WinRAR Example: CVE-2025-8088 and the Malicious msedge.dll
The recent WinRAR path traversal vulnerability (CVE-2025-8088) is a textbook case for why runtime behavioral analysis is essential for modern threat detection. In this attack, a specially crafted archive can exploit WinRAR’s handling of file extraction to write a malicious DLL, in the case of RomCom group, a DLL named msedge.dll
, directly into the user’s %TEMP%
directory. What makes this attack particularly insidious is its use of an Alternate Data Stream (ADS): the archive contains a file path like Eli_Rosenfeld_CV.pdf:.\..AppDataLocalTempmsedge.dll
, which is hidden by default from view, but when executed, causes WinRAR to create the msedge.dll
file in %TEMP%
with attacker-controlled content.
This isn’t just a matter of a single unsafe function call. The exploitability hinges on the entire execution flow: how WinRAR parses archive entries, how it interprets NTFS ADS syntax, what (if any) validation or sanitization is performed on file paths, and how those paths are ultimately resolved and written to disk. In the case of CVE-2025-8088, insufficient sanitization allowed the crafted path to bypass intended restrictions, resulting in arbitrary file write.
Traditional vulnerability scanners might flag the use of file write operations, but in the case of an archive extraction utility, this is expected behavior. Static analysis could highlight potentially risky code for the development team, but few organizations run third-party applications through static analysis. Considering the vast majority of third-party software is delivered as compiled binaries, this isn’t even possible.
Runtime behavioral analysis, on the other hand, can observe the full source-to-sink chain as it happens. It can track how untrusted archive input is parsed, how path strings are manipulated, and whether those strings reach sensitive file system operations without proper sanitization. In the WinRAR exploit, behavioral analysis would reveal the moment when a crafted archive entry leads to the creation of msedge.dll
in %TEMP%
, which is a clear indicator of compromise that is invisible to static or signature-based tools.
This level of runtime visibility doesn’t just show that a vulnerability exists; it demonstrates that the vulnerability is actively exploitable under real-world conditions, and it can pinpoint the exact behavioral chain that leads to compromise.
Multi-Event Co-Reasoning: Connecting the Dots
The most sophisticated aspect of behavioral intelligence is what I call multi-event co-reasoning: the ability to correlate seemingly unrelated runtime events to identify complex attack patterns or vulnerability conditions.
Consider a scenario where an application performs these seemingly benign operations:
- Reads configuration data from a network source
- Processes that data through a parsing routine
- Uses the processed data to determine file paths for logging
- Creates files at those computed paths
Individually, none of these operations appears suspicious. Configuration updates, data processing, logging, and file creation are all normal application behaviors. But when analyzed together through the lens of behavioral intelligence, this sequence reveals a potential arbitrary file write vulnerability – one that might never be documented in CVE databases or caught by traditional scanners.
This type of multi-event analysis is only possible with runtime visibility. You need to observe the actual execution flow, track data as it moves between operations, and understand the relationships between different program behaviors. Static analysis can’t provide this level of insight because it can’t account for runtime conditions, input variations, or the complex interactions between different code paths.
The Diagnostic Analytics Advantage
What we’re really talking about here is diagnostic analytics at a level of sophistication that has never existed in the security industry. Traditional security tools provide detection – they tell you when something bad has happened. Runtime behavioral intelligence provides diagnosis – it tells you why something is vulnerable, how it could be exploited, and what conditions would prevent that exploitation.
This diagnostic capability creates several strategic advantages:
Proactive Risk Identification: Instead of waiting for exploits to be discovered and disclosed, security teams can identify exploitable conditions as they emerge in their environment.
Contextual Prioritization: Not all vulnerabilities are equally dangerous in every environment. Behavioral analysis can determine which vulnerabilities are actually exploitable given the specific configuration, access controls, and usage patterns in your organization.
Preventive Control Design: When you understand the complete behavioral chain that leads to exploitation, you can design targeted controls that break that chain at the most effective points.
Zero-Day Resilience: Because behavioral analysis focuses on risky patterns rather than known signatures, it can identify novel attack techniques that haven’t been seen before.
Getting Ahead of Adversaries for the First Time
For too long, enterprise security has been a reactive discipline. We patch after vulnerabilities are disclosed. We update signatures after attacks are documented. We implement controls after breaches are analyzed. This reactive cycle keeps us perpetually behind attackers who are actively exploring new techniques and exploiting unknown vulnerabilities.
Runtime behavioral intelligence changes this dynamic. By analyzing the actual execution patterns of software in real-time, security teams can identify risky behaviors before they’re exploited. They can spot vulnerable code paths before they’re documented. They can recognize attack techniques before they’re widely known.
This isn’t theoretical. In our work at Spektion, we regularly identify exploitable conditions in software that haven’t been assigned CVEs and may never be. We find privilege escalation opportunities, data exfiltration risks, and remote code execution possibilities that exist in the behavioral patterns of software, not in their documented vulnerabilities.
The Technical Reality of Behavioral Analysis
Implementing effective behavioral intelligence requires significant technical sophistication. You need lightweight instrumentation that can observe program execution without impacting performance. You need analytics engines that can process complex event streams in real-time. You need correlation algorithms that can identify meaningful patterns among vast amounts of behavioral data.
Most importantly, you need a deep understanding of how exploitation actually works. Building effective behavioral detection isn’t just an engineering challenge – it requires expertise in offensive security, vulnerability research, and threat modeling. You have to know what attackers look for in order to build systems that can identify those same conditions proactively.
This is why most attempts at „next-generation“ security solutions still fall back on signature-based approaches. Building true behavioral intelligence is harder than optimizing existing detection methods or creating better dashboards for known vulnerabilities. But it’s also the only approach that can fundamentally change the security advantage.
A New Category of Security Advantage
The organizations that embrace runtime behavioral intelligence aren’t just getting better detection – they’re gaining a fundamentally different type of security advantage. For the first time, they can identify and address risks before those risks become known attack vectors. They can implement targeted controls based on actual software behavior rather than theoretical vulnerability assessments.
This approach requires investment in new technologies and new expertise. It demands a shift from reactive security operations to proactive risk management. But for organizations serious about staying ahead of sophisticated adversaries, behavioral intelligence represents the clearest path forward.
The threat landscape has evolved beyond what signature-based detection can effectively address. Runtime behavioral analysis doesn’t just offer better detection – it offers the possibility of finally getting ahead of the problem instead of constantly reacting to it.
That’s a strategic advantage that changes everything.
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