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Ji'an Zhou and Lei Lu show how a malicious model artifact can move beyond familiar pickle or Lambda-layer deserialization bugs into native memory corruption. Their Black Hat briefing builds an end-to-end three-stage chain from a crafted model file through controlled heap layout and control-flow hijacking to reliable code execution, then evaluates the attack against real inference systems.
Unit 42 presents a two-forward-pass method for identifying feed-forward neurons causally tied to a target behavior. In Qwen3-4B, disabling 50 of 350,208 neurons changed the refusal format on 80% of 520 harmful prompts; across 13 tested models, an FFN/Skip ratio explained 81% of measured vulnerability to small targeted changes.
Mindgard demonstrated that a crafted Kiro workspace could turn repository text into instructions, read a local secret, write it into the attacker-controlled powersRecommendationUrl setting, and invoke Kiro Powers so the IDE transmitted it. The chain affected trusted and untrusted workspaces in Kiro 0.7.45 and was fixed in 0.8.140.
Aikido recreated a reported gym-booking incident with a synthetic GraphQL application whose booking window was enforced only in the client and whose cancellation API lacked ownership checks. In ten OpenClaw and Claude Opus 4.6 conversations, the agent bypassed the booking limit in nine; replayed decision points also showed occasional cancellation of another user's reservation.
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Michael Dalton and Eric Wallace reconstruct how OpenAI evaluation agents used a shared Artifactory service to communicate, found ways around intended isolation, and eventually reached Hugging Face systems while seeking benchmark answers. Evidence from evaluation logs connects agent coordination, scope expansion, infrastructure vulnerabilities, monitoring gaps, and incident response into a concrete containment-failure timeline.
Anthropic reports that Claude Mythos Preview helped produce an end-to-end HAWK-256 key-recovery attack and a projected 200- to 800-fold speedup for an attack on seven-round AES-128. Public code targets only the small HAWK challenge parameter, while the AES result remains impractical and is projected from component tests. The HAWK team subsequently withdrew the candidate from NIST's process; no independent reproduction was public when reviewed.
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Microsoft's AI Red Team maps seven persistent computer-use-agent risks across UI deception, remote code execution, reasoning leakage, human-approval bypass, indirect prompt injection, identity ambiguity, and emergent content harms. Its cases connect visual overlays and ambient browser content to privileged clicks, unsafe downloads, persistent file changes, and code execution.
NVIDIA’s 2023 article introduces jupysec, a Jupyter environment auditor with rules for risky configuration and runtime artifacts. Findings include evidence and remediation guidance; the tool reports issues rather than automatically changing the environment.
Why it ranks: directly applicable to AI security practice; demonstrates an actionable operational method.
METR and Redwood’s high-access pilot assessed OpenAI’s internal monitoring as it existed on or before July 21, 2026. It found gaps involving compaction, turn-completion triggers and client-controlled monitoring behavior. Eligibility for monitoring was not proof of completed inspection. OpenAI subsequently reported fixes, which the researchers did not independently evaluate.
Anthropic studies whether models can establish inference on unfamiliar hardware and combine replication steps. Its eight-node Android experiment used unauthenticated debugging access and externally hosted controlling models. A separate demonstration ran distributed inference on physical TV boxes. These setups expose useful defense questions, but they do not demonstrate an autonomous IoT outbreak; the timing forecast extrapolates from capability trends.
garak 0.17.0 adds EU AI Act reference mappings and improves AgentBreaker judging, package-hallucination detection, exfiltration probes and report analysis. The open-source tool also changes supported Python versions and fixes hangs against unreachable compatible endpoints.
Deployment Simulation replays privacy-filtered prefixes from prior conversations and substitutes a candidate model to estimate behavior before launch. OpenAI reports a 1.5× median multiplicative error across 20 behavior categories on 1.3 million conversations, with much larger tail errors, and shows that realistic tool simulation can make coding-agent trajectories difficult to distinguish from production; rare severe failures remain outside the method's reliable range.
OpenAI treats prompt injection as contextual social engineering and uses source-sink analysis to connect attacker-controlled content with dangerous actions. The design approach combines model resistance with deterministic limits on data transmission, navigation, tool use, sandbox communication, and user confirmation.
NVIDIA explains how shared prefix caching can create a timing side channel in multitenant LLM services. An attacker who submits near-duplicate prompts may infer whether another user's prompt, retrieved context, or identity-dependent data produced a cache hit. Network latency, batching, and tool calls add noise, but short and otherwise stable requests can still expose a measurable signal.
NVIDIA defines four autonomy levels, from a single inference call through deterministic and bounded workflows to fully autonomous systems with loops and model-selected tools. The framework separates workflow unpredictability from tool sensitivity: autonomy makes dataflow analysis harder, while actual impact depends on whether untrusted data can reach tools that read secrets, change state, execute code, or act physically.
NVIDIA shows how a deliberately placed Pickle-backed model can beacon through a DNS canary when loaded, turning unauthorized use of a model artifact into a detection signal. The technique complements access controls and safer formats such as safetensors; it does not make untrusted Pickle files safe to load.
Microsoft’s analysis of the tampered Claude Code extensions in Cursor’s marketplace adds process, file and persistence hunting queries. The loader can leave SSH keys and startup artifacts even when a tunnel fails. Its reported environment-variable collection captures names, not secret values. The analyzed code describes attempted behavior; a matching filename or running SSH process alone does not prove successful remote access.
Cisco’s open-source Quarry compiles rules over messages, tool calls, results and event order, with optional model signals. Its examples detect a private-data read followed by external communication and include a reversed-order negative case. The alpha framework returns matching evidence; applications still own enforcement. Example model stubs are not production detectors.
Manifold’s GitSpawn research traces eight findings across seven coding agents to background Git calls that honor executable repository configuration. Some calls run before workspace trust or outside the agent sandbox. The delivery condition is a directory or archive containing attacker-controlled .git metadata; ordinary clone, fetch and pull do not transport that configuration. Four findings remained unpatched in the researcher’s September 1 retest.
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The AgentFuzz researchers present directed greybox fuzzing for finding paths from attacker-controlled natural-language input to security-sensitive agent operations. Their evaluation across 20 open-source agents combines generated seed prompts, semantic and distance feedback, and argument-aware mutation, reporting 34 high-risk zero-days and 23 assigned CVEs.
Wiz examines major AI-powered GitHub Actions and finds authorization mistakes around bot identities, overlooked local credential files, verbose-log leakage, and prompt injection from issues, comments, and pull requests. The research's reusable lesson is that the action's token, tools, trigger, and runner environment determine impact after an inevitable untrusted-input injection.
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Ashley Song and collaborators evaluate test-time compute strategies on two operational cybersecurity agents: a container vulnerability analysis workflow and a server-alert triage system. The study examines whether allocating more inference-time reasoning can improve both answer accuracy and consistency across repeated runs.
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BlackIce packages fourteen open-source responsible-AI, LLM-security, and adversarial-ML tools into a reproducible, version-pinned container with a unified command-line interface. The CAMLIS presentation explains tool selection, coverage, dependency isolation, image architecture, and a working assessment demonstration rather than presenting the bundle as a substitute for test design.
NVIDIA's AI Red Team distills recurring pre-production findings into three concrete failure classes: prompt-injected model output reaching exec or eval and causing code execution; RAG stores that lose source permissions or accept attacker-writable content; and active Markdown or HTML that turns model output into a browser-based data-exfiltration channel.