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Episode #1094 Quiz

AI Patching Shortcomings
Date: 2026-09-01 | Length: 2.75 hrs | Episode page at twit.tv

About this episode

Researchers found LLM patch generation is weak: only 26% of fixes fully remediated vulnerabilities without side effects, while others left exploitable paths, changed behavior, or introduced new bugs. Correct, root-cause-rich prompts and iterative harnesses helped most; incorrect guidance caused collapse. Human expert review remains necessary before deploying AI-generated patches.

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Question 1: In the proposed role-confusion mitigation, what exact condition should cause the dialogue manager to abort the current work?

Question 1

In the proposed role-confusion mitigation, what exact condition should cause the dialogue manager to abort the current work?

A detected disparity between the last role tag sent and the model's current belief about the text role Any tool response that includes a command-like sentence in plain language Any instance of a user prompt containing a <tool> tag pair Any output token whose probability exceeds the harness's safety threshold

Question 2: Which statement best captures the protocol behavior VulnCheck observed for the ENDLESSDOORS implant on Zbtlink routers?

Question 2

Which statement best captures the protocol behavior VulnCheck observed for the ENDLESSDOORS implant on Zbtlink routers?

It registers with a fixed 39-byte hello and then accepts root commands sent by the server It performs mutual authentication before opening an encrypted management channel It validates each command against an allow-list before execution as uid 0 It waits for inbound connections on a listening TCP port exposed to the internet

Question 3: What were the two major factors the FLAWED paper identified as most strongly affecting patch success rate?

Question 3

What were the two major factors the FLAWED paper identified as most strongly affecting patch success rate?

Model size and tool latency Guidance correctness and prompt richness Git history depth and compiler version Iteration count and review team size

Question 4: In the FLAWED study, what was the combined outcome percentage for patches that either failed to fix the original vulnerability or introduced a new security vulnerability?

Question 4

In the FLAWED study, what was the combined outcome percentage for patches that either failed to fix the original vulnerability or introduced a new security vulnerability?

4.5% 49.3% 26.0% 51.5%

Question 5: What did the paper recommend when remediation guidance may be inaccurate, such as when it comes from another automated tool?

Question 5

What did the paper recommend when remediation guidance may be inaccurate, such as when it comes from another automated tool?

Omit lower-confidence guidance and provide only the bug Expand the prompt with more tool output until the model can reconcile the inconsistency Let the patcher agent cross-check the upstream patch directly Prefer one-shot mode because it is more robust against incorrect guidance

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