GlobaLeaks, a mature whistleblowing platform that had already undergone six independent professional audits over the past thirteen years, was subjected to an LLM-assisted security review that cost roughly USD 3,140 in API calls. The review identified 29 confirmed vulnerabilities, 12 denial-of-service issues, and 42 hardening recommendations, with an average cost of about USD 77 per confirmed finding before human validation.
The most important point is probably the cost. Reading an entire codebase systematically, line by line and against major known weakness classes, traditionally required weeks of specialist work and a serious budget. That assumption no longer holds in the same way: the report argues that this kind of analysis is now far more accessible than it used to be.
“The distinction matters because it changes who a defender has to worry about. For most of the history of software, the close reading of a large codebase was a scarce and expensive skill; the set of people who could do it was small, and the effort priced casual adversaries out.” reads the report.
The review was not run against neglected software. According to the report, the maintainers had landed 183 commits in the month before the reviewed snapshot during an intensive hardening and release cycle that included token hashing, session-state resets, tighter authorization, and new audit logging. That matters because findings uncovered in a codebase at one of its better-defended moments carry more signal than issues found in stale or abandoned software.
The distribution of cost across models was also revealing. One high-reasoning model accounted for 61.9% of total spend while processing only about 90 million of the 1.24 billion tokens used in the campaign, while cheaper models handled most of the broad reading volume at much lower cost. In other words, deeper reasoning was more expensive, but the gap was no longer large enough to act as a serious barrier.
“The capability is real, and by the standards of any motivated adversary it is inexpensive.” states GlobaLeaks.
The review produced 110 triaged records in total: 29 confirmed vulnerabilities, 12 denial-of-service findings, 42 hardening recommendations, and 27 retained non-findings kept for transparency. That choice matters because it shows not only what was found, but also what was considered and later set aside, which is a healthier way to present LLM-assisted research than pretending every model output is meaningful.
Some of the most important findings were not exotic at all. The report describes issues involving session-to-account takeover paths, whistleblower anonymity risks, tenant-boundary weaknesses, missing audit trails for sensitive actions, and availability problems that a single unauthenticated user could trigger. That is precisely what makes the result uncomfortable: the value of the LLM-assisted approach is not that it discovers magic bugs, but that it makes broad, patient, systematic reading cheap enough to be repeated at scale.
“What is striking about these findings is how ordinary most of them are. They are not exotic cryptographic breaks or novel exploit primitives.” continues the report. “They are missing checks, mutable identifiers, unlogged actions – the small, individually forgivable mistakes that accumulate in every large codebase and that no amount of prior auditing fully removes.”
The report is also careful not to oversell the machine. Every candidate produced by the models was treated as a hypothesis until a human reviewer traced it through the code, reproduced it where needed, and assessed its practical impact. The machine reduced the cost of looking, but it did not replace expert judgment.
“None of this means the machine has replaced the expert. It has not: separating 29 real vulnerabilities from a much larger heap of plausible-looking noise took human judgment at every step.” reads the report. “What has changed is the price of looking.”
That is the real takeaway for teams building or defending critical software. A project that protects people at real risk can no longer assume that thorough code reading is too expensive for most adversaries, because commercial LLMs have changed that equation. The practical response is the one the report itself points to: continuous hardening, disciplined review, and the assumption that the next entity reading the code may be cheaper, faster, and more patient than the last.
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(SecurityAffairs – hacking, GlobaLeaks)