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When Models Edit Too Much: On the Fidelity of Minimal Code Edits

A 400-task BigCodeBench evaluation shows frontier LLMs widely over-edit code; a preservation instruction cuts excess edits and raises Pass@1 by 2.3 points.

Researchers built an evaluation framework from 400 BigCodeBench problems with injected AST-level corruptions, each with a known minimal patch, to measure over-editing in LLM code repair. Even strong models like GPT-5.5 produce unnecessarily large edits despite high Pass@1. Adding a preservation instruction reduced average excess Levenshtein distance from 0.195 to 0.131, cut added cognitive complexity by 26.6%, and raised Pass@1 by 2.3 points. Reinforcement learning post-training gave the best out-of-domain edit-fidelity trade-off, while supervised fine-tuning overfit to seen corruption patterns.

Hugging Face daily papers · 14d agoAI research1

ZDI-26-679: Adobe Photoshop DCM JPEG Image Parsing Integer Overflow Remote Code Execution Vulnerability

ZDI disclosed a CVSS 7.8 integer overflow remote code execution flaw (CVE-2026-75862) in Adobe Photoshop DCM JPEG image parsing.

The Zero Day Initiative published advisory ZDI-26-679 describing an integer overflow remote code execution vulnerability in Adobe Photoshop, tracked as CVE-2026-75862 with a CVSS 7.8 score. The flaw occurs while parsing JPEG data in DCM images. Exploitation requires user interaction: the target must visit a malicious page or open a malicious file. No in-the-wild exploitation is reported.

ZDI-26-658: Adobe Acrobat Pro DC JPEG Parsing Integer Overflow Remote Code Execution Vulnerability

ZDI discloses CVE-2026-81987, an integer overflow in Adobe Acrobat Pro DC JPEG parsing enabling remote code execution with CVSS 7.8.

The Zero Day Initiative published ZDI-26-658 covering an integer overflow in Adobe Acrobat Pro DC's parsing of JPEG files. Successful exploitation allows arbitrary code execution but requires user interaction, such as opening a malicious file or visiting a malicious page. ZDI assigned a CVSS score of 7.8 and tracked the flaw as CVE-2026-81987.

One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing

Researchers introduce EditVid, a training-free video editing framework scoring 78.16 FiVE-Acc versus 58.95 for the strongest comparable baseline.

EditVid is a unified training-free framework for diverse instruction-guided and subject-guided video editing. It combines sparse causal memory for local coherence, correspondence-based post-attention token injection for long-range identity preservation, and soft latent blending for edit locality. On the FiVE benchmark it reaches 78.16 FiVE-Acc against 58.95 for the strongest evaluated training-free baseline, with competitive results on IVEBench. A user study showed 51.8% overall preference for EditVid over 7 competing methods.

Hugging Face daily papers · 14d agoAI research

Vulnerabilities fixed in libxml2-2.15.4

libxml2 2.15.4 fixes an out-of-bounds read in xmlregexp's NXT macro plus several integer overflow and parsing flaws.

libxml2 2.15.4 (released September 1, 2026) includes security fixes: an out-of-bounds read in the xmlregexp NXT macro, missing overflow checks in dict.c, uri.c, and valid.c, an integer overflow in xmlIO before the writecallback, and an overflow check in xmlXPtrEvalXPtrPart. The release also propagates parseFlags in xmlXIncludeProcess and xmlXIncludeProcessTree. No CVE identifiers, exploitation, or severity ratings are given in the announcement.

oss-security · 12d agoVulnerability

Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models

Empirical study finds direct whole-file generation beats iterative diff-based editing for Flutter/Dart code models on about 1,790 held-out tasks.

Researchers trained Rainbow-Pony-100M from scratch and fine-tuned Qwen2.5-Coder-0.5B in both direct-generation and diff-based regimes, then evaluated four resulting models on roughly 1,790 Flutter/Dart tasks. Direct generation outperformed diff-based generation on compilation pass rate, bits-per-byte, character-level similarity, and blinded LLM-judge ratings. Diff-based editing is competitive only on short, localized edits in refactoring and error-handling tasks, a property the authors call task locality.

Hugging Face daily papers · 12d agoAI research1

Re: Vulnerabilities fixed in libxml2-2.15.4

libxml2 2.15.4 patches two flaws including a heap buffer overflow in xmlDictAddQString tracked as CVE-2026-86137 and CVE-2026-86138.

libxml2 releases before 2.15.4 are affected by an integer overflow in xmlDictAddQString in dict.c that leads to a heap-based buffer overflow, tracked as CVE-2026-86137 and CVE-2026-86138. The oss-security post from Debian's Salvatore Bonaccorso flags the fixed release for downstream tracking. No exploitation is mentioned in the disclosure.

WireGuard-Linux Stack-Based Buffer Overflow in lsiio (Linux IIO Userspace Tool) Due to Unbounded fscanf

The Linux IIO userspace tool lsiio has a stack buffer overflow in find_type_by_name() caused by unbounded fscanf reads of oversized filesystem-backed attribute values.

A stack-based buffer overflow exists in the Linux Industrial I/O (IIO) userspace utility lsiio. In the find_type_by_name() function, the program reads an unbounded string from a filesystem-backed attribute into a fixed-size stack buffer using fscanf("%s", ...). A crafted or oversized attribute value causes a write beyond the bounds of the destination buffer. Despite the title's reference to WireGuard-Linux, the flaw described is in the IIO lsiio utility.

Full Disclosure · 12d agoVulnerability

Claude is a Contrarian

Opinion piece argues Claude habitually contradicts explicit user instructions, injecting contrarian content despite CLAUDE.md rules and user objections.

A developer recounts repeated instruction-following failures with Claude, claiming it contradicts explicit requests, adds unnecessary work, and ignores AGENTS.md and CLAUDE.md directives. The author contrasts this with OpenAI, DeepSeek, and Qwen models, which he says more readily apologize and undo mistakes. He theorizes Claude's training makes it assume the human is wrong and needs correcting. The post is personal commentary with no benchmarks or systematic evaluation.

Heap overflow in kernel driver due to missing size validation

Fortinet fixes a CVSS 7.3 heap overflow in the FortiClient Windows kernel driver enabling code execution via crafted DNS responses.

Fortinet PSIRT advisory FG-IR-26-156, revised 2026-08-12, describes a heap-based buffer overflow (CWE-120, buffer copy without checking input size) in the FortiClient Windows kernel driver, scored CVSSv3 7.3. An unauthenticated attacker positioned to alter or craft DNS responses for a targeted host could execute arbitrary code via malicious packets. No CVE identifier or exploitation status is provided in the advisory text, so administrators should check the full bulletin for affected versions and fixed releases.

Fortinet PSIRT · Aug 12, 2026Advisory

thttpd v2.26 Stack-Based Buffer Overflow in thttpd redirect CGI Program

The redirect CGI program shipped with thttpd v2.26 has a stack buffer overflow that unauthenticated attackers can trigger for crashes or possible code execution.

A stack-based buffer overflow exists in the redirect CGI program distributed with thttpd v2.26. Unsafe string concatenation when constructing redirect URLs from attacker-controlled CGI environment variables causes the overflow. A remote, unauthenticated attacker can trigger it via a crafted HTTP request, crashing the CGI process and causing denial of service. In environments lacking modern exploit mitigations, code execution may also be possible.

Full Disclosure · 12d agoVulnerability

LexFlip: A Dissociation Diagnostic for Legal Meaning Preservation Metrics

LexFlip releases 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving tokens, exposing weaknesses in embedding-based meaning preservation metrics.

LexFlip provides 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving 0.93 of tokens, creating dissociation items that break monotone token-overlap metric validation. The seven embedding and BERTScore metrics tested register only 0.022-0.039 of their identical-to-unrelated range on these edits, versus 0.670 for bidirectional NLI. Against FrJudge, with a measured human ceiling of r=0.597, a bare length feature outscores every semantic metric tested.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research

What Else Needs Fixing? Exploring Cost-Effective Test-Time Compute for Revision Propagation in Artifacts Generated Through Conversation

A new benchmark shows LLMs reach 68.3-93% accuracy propagating local revisions across conversationally generated artifacts, with parallel-sample selection most cost-effective.

The paper introduces a benchmark for revision propagation: when users request a local change, LLMs must identify dependencies and update all affected parts of an artifact generated through conversation, where context lives in the chat history. Nine revision methods, including sequential reflection and parallel sampling variants, were evaluated on gpt-oss-20b/120b, gpt-5.4-mini, and qwen3.5-9b/27b/122b. Baselines scored 68.3-93% accuracy, and selecting among three parallel samples via LLM-based or medoid selection improved accuracy by 2.2-9.7% as the most cost-effective test-time compute strategy. Code and dataset are released.

Hugging Face daily papers · 14d agoAI research

USN-8772-1: AOM vulnerabilities

Ubuntu USN-8772-1 patches four libaom flaws (CVE-2026-56208 to CVE-2026-56211) that could cause heap overflow, arbitrary memory writes, or code execution.

Ubuntu Security Notice USN-8772-1 fixes a heap buffer overflow in libaom's first-pass statistics buffer handling in Look-Ahead Processing mode (CVE-2026-56208), potentially causing denial of service or arbitrary code execution. Three additional flaws in spatial and temporal layer ID validation in the SVC encoder controls (CVE-2026-56209, CVE-2026-56210, CVE-2026-56211) allow arbitrary memory writes, out-of-bounds heap reads, or code execution. Users should apply the updated packages.

Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability

A controlled study finds agent memory portability varies sharply: fixed-schema knowledge graphs survive model swaps while compressed notes degrade.

The study compares preserving an agent's history as raw long context, RAG chunks, compressed natural-language notes, or fixed-schema knowledge graphs across model upgrades, using 48 synthetic histories and two open-weight sub-10B-parameter models. Fixed-schema KG accuracy changed by only +0.0004 ± 0.0020 after a writer swap, while compressed NOTES shifted asymmetrically by +9.91 or -13.28 percentage points depending on migration direction. Mixed 50/50 embedding migrations captured only 4.96 of an 11.90-point RAG re-embedding gain; 80% of the NOTES deficit came from information lost at construction, and 81% of the RAG deficit from retrieval failures. Store-only repair of NOTES failed to reach 90% recovery in all 48 cases, while retaining raw histories enabled recovery in 34 of 48 for one direction.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research1

New AI Attack Hides Malicious Instructions in Normal-Looking Text to Evade Safety Filters

Check Point researchers show crafted prose hides policy-violating instructions that bypass all tested LLM gatekeepers, including GPT-4o mini and Llama Guard 3.

A new prompt-crafting technique embeds malicious payloads inside grammatical, natural-looking text without Base64, invisible Unicode, or obvious encodings, defeating lightweight pre-screening gatekeepers. In testing, all four evaluated gatekeeper models—gpt-4o-mini-2024-07-18, gpt-oss-safeguard:20b, claude-3-haiku-20240307, and llama-guard3:8b—classified the crafted wrappers as safe at a 100% bypass rate across 23 obfuscated prompts. GPT-5 Thinking in high-reasoning mode recovered and acted on the hidden instruction in 17 of 18 tests (~94.4%), often spending over a minute and multiple Python executions. Researchers recommend paraphrasing untrusted input, hardening gatekeeper policies, and applying defense-in-depth controls for agentic deployments.

GBHackers · 5d agoAI safety & security 2 sources

Mi-Ripple: Restoring Images Degraded by Iterative AI Editing

Mi-Ripple is a diagnosis-guided restoration workflow that removes digital ripple artifacts introduced by iterative AI image editing while preserving structure.

Iterative reference-conditioned image editing can introduce grid-like and granular textures known as digital ripple. Mi-Ripple separates periodic lattice artifacts from content-entangled granular texture, then applies selective spectral notching, structure-aware smoothing, and cleaned-reference regeneration. In fourteen notch-only executions, whole-image residual standard deviation was 0.08-0.44 in CIELAB lightness units, and reference cleaning reduced output debris density by 45% in a paired example.

Hugging Face daily papers · 7d agoAI research

Context Engineering Inside the Harness: 4 Mechanisms That Beat Context Overflow and Goal Loss on Long-Horizon Tasks

Survey of four harness mechanisms—context budgeting, compaction, todo-state, and memory—that keep long-horizon LLM agents on task across 200+ tool calls.

The article details how agent harnesses, not larger context windows, solve context overflow and goal loss on long-horizon tasks, citing Chroma's Context Rot report showing 18 LLMs (GPT-4.1, Claude 4, Gemini 2.5, Qwen3) degrade on long inputs. Concrete implementations include LangChain Deep Agents offloading tool responses over 20,000 tokens to the filesystem and truncating old tool calls at 85% window usage, and Claude Code capping auto memory at 25KB while re-reading the 5 most recently modified files after compaction. OpenAI's Responses API now offers server-side compaction via context_management with a standalone /responses/compact endpoint, which Codex uses for long-running coding tasks. Manus reports a roughly 100:1 input-to-output token ratio per ~50-tool-call task, motivating todo.md state recitation to prevent goal drift.

MarkTechPost · 3d agoAI research1

Why don't machine learning research agents overfit?

Amazon researchers explain why ML research agents avoid benchmark overfitting, attributing generalization to compressibility of successful strategies.

Amazon Science summarizes the paper "What fits (into few tokens) doesn't overfit: Compression and generalization in ML research agents," which investigates why benchmark hill-climbing loops, whether run by human communities or LLM research agents, do not produce rampant overfitting. The explanation formalizes Occam's razor via a counting argument: successful ML strategies are highly compressible, so short descriptions lack room to memorize benchmark data and must capture real structure. LLM-based agents, being resettable and controllable, allow this hypothesis to be tested empirically.

Zoom Annotation Flaws Could Let a Meeting Participant Hijack Another Attendee's Client

A Security researchers disclosed three Zoom annotation flaws enabling zero-click client hijacking; Zoom shipped fixes in June and July with no exploitation reported.

Researchers at A Security found three flaws in Zoom's annotation feature: CVE-2026-53413 (CVSS 8.3, buffer over-write), CVE-2026-53414 (CVSS 6.5, buffer over-read), and CVE-2026-53415 (CVSS 8.3, use-after-free). A crafted drawing object sent over the wrong message channel can overwrite adjacent memory and hijack another attendee's client with no user interaction. Fixes shipped in Zoom Workplace 7.1.5/7.0.6, VDI Client 7.0.11/6.6.16, and Zoom Rooms/Meeting SDK 7.1.0+ during June and July. No exploitation has been reported and the flaws are absent from CISA's Known Exploited Vulnerabilities catalog.

Hiding Prompt Injection in Legal Filing

A judge banned a plaintiff from electronic court filings after hidden prompt-injection text was discovered planted in legal documents.

Bruce Schneier's blog discusses an incident in which hidden prompt-injection instructions were planted inside a legal filing, apparently targeting AI systems that might process court documents. Judge Walter Spader Jr. responded by banning the plaintiff from electronic filings, requiring all future submissions as printed hard copies. Commenters debate whether the tactic could affect future AI-based processing of court records and whether plain-text formats will regain favor.

Schneier on Security · 16d agoAI safety & security in the wild

Zoom Patches “Zoomsday” Zero-Click Flaw Enabling Remote Code Execution

Zoom patched CVE-2026-53413, a zero-click annotation flaw dubbed "Zoomsday" allowing remote code execution on meeting participants' devices across all platforms.

Zoom patched four vulnerabilities, including CVE-2026-53413, a stack buffer overflow in CAnnoFormatBlock::Deserialize in the annotation protocol that allows zero-click remote code execution on another participant's device. A Security also found CVE-2026-53414, a buffer overread enabling denial-of-service crashes, and CVE-2026-53415, a use-after-free Zoom had already discovered internally. Updates shipped for Workplace 7.1.5 and 7.0.6, Rooms 7.1.5, and Meeting SDK 7.1.5 across all supported platforms.

ZDI-26-635: Oracle Outside In Technology PDF File Parsing Integer Overflow Remote Code Execution Vulnerability

ZDI disclosed CVE-2026-60392, an integer overflow in Oracle Outside In PDF parsing enabling remote code execution, rated CVSS 7.8.

The Zero Day Initiative published advisory ZDI-26-635 describing an integer overflow vulnerability in PDF file parsing within Oracle Outside In Technology. Successful exploitation allows remote code execution but requires user interaction, such as opening a malicious file or visiting a malicious page. ZDI assigned the flaw a CVSS rating of 7.8.

[remote] PCMan 2.0.7 - Buffer Overflow

A remote buffer overflow in PCMan 2.0.7 has a public proof-of-concept exploit published on Exploit-DB.

Exploit-DB listing 52657 discloses a remote buffer overflow vulnerability in PCMan version 2.0.7. A proof-of-concept exploit is publicly available, allowing attackers to potentially crash or compromise affected instances. The listing includes no CVE identifier and reports no observed exploitation in the wild.

Exploit-DB · 29d agoExploit / PoC1

Bad Likert Judge: A Novel Multi-Turn Technique to Jailbreak LLMs by Misusing Their Evaluation Capability

Unit 42 details the Bad Likert Judge multi-turn jailbreak that abuses LLMs' evaluation capability, raising attack success rates over 60% across six frontier models.

Palo Alto Networks Unit 42 describes the Bad Likert Judge technique, a multi-turn jailbreak that asks a target LLM to act as a Likert-scale judge scoring the harmfulness of example responses. The highest-rated example in each scale can carry harmful content, bypassing the model's internal guardrails. Testing across six state-of-the-art text-generation LLMs showed an average attack success rate increase of more than 60% versus plain attack prompts, with tested models anonymized. The technique targets edge cases rather than typical use, and the article positions the work as guidance for defenders on potential jailbreak risks.

Palo Alto Unit 42 · Aug 17, 2026AI safety & security

Adobe is trying to make its AI generators idiot-proof in Premiere

Adobe adds in-timeline generative media to Premiere, letting editors generate video and audio clips using Firefly, Veo, Runway, Luma, and Kling models.

Adobe's new Generative Media tool lets Premiere editors highlight empty gaps in the timeline and generate context-aware, editable video, sound effects, music, and soundscapes without leaving the project. Editors can choose among underlying models including Adobe Firefly, Google Veo, Runway, Luma, and Kling. Beta AI audio tools can separate overlapping speakers and duck music under speech, and an AI Assistant is coming to After Effects for plain-language project commands.

The Verge · AI · 8d agoAI industry

USN-8737-1: GNU C Library vulnerabilities

Ubuntu patches GNU C Library flaws including a strfmon buffer overflow and tdelete out-of-bounds access that could enable code execution.

Ubuntu issued USN-8737-1 fixing multiple GNU C Library vulnerabilities. CVE-2026-19499 is a buffer overflow in the strfmon function when handling right-justification padding, affecting only Ubuntu 26.04 LTS. CVE-2026-19542 is an out-of-bounds stack array access in the tdelete function. An additional issue involves incorrect memory handling in wordexp with the WRDE_APPEND flag. All issues could potentially cause denial of service or arbitrary code execution.

The GNU C Library security advisories update for 2026-09-14

glibc advisory GLIBC-SA-2026-0017 discloses a buffer overflow in strfmon/strfmon_l affecting versions 2.38 through 2.44.

The GNU C Library published security advisories including GLIBC-SA-2026-0017, a buffer overflow in strfmon and strfmon_l. Calling these functions with right-justified width padding conversions can write past the end of the caller-supplied output buffer in glibc 2.38 to 2.44. Exploitation requires an application code path that calls strfmon with attacker-influenced parameters.

oss-security · 2d agoVulnerability 2 sources1